Introduction¶
What is Blockchain¶
By allowing digital information to be distributed but not copied, blockchain technology created the backbone of a new type of internet. Originally devised for the digital currency, Bitcoin, (Buy Bitcoin) the tech community has now found other potential uses for the technology.
Library¶
In [1]:
from numpy import *
import numpy as np
import pandas as pd
from scipy import stats
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import GRU
from keras.layers import Dropout
from keras.callbacks import EarlyStopping
from keras import initializers
from matplotlib import pyplot
from datetime import datetime
from matplotlib import pyplot as plt
import plotly.offline as py
import plotly.graph_objs as go
from sklearn.preprocessing import MinMaxScaler
%matplotlib inline
Using TensorFlow backend.
In [2]:
from IPython.display import IFrame
IFrame(src='https://coinmetrics.io/data-downloads/', width=900, height=400)
Out[2]:
In [3]:
dataset = pd.read_csv('ltcForTrain.csv')
In [4]:
print ("Data shape : ",dataset.shape)
dataset.head(5)
Data shape : (2744, 17)
Out[4]:
| date | txVolume(USD) | adjustedTxVolume(USD) | txCount | marketcap(USD) | price(USD) | exchangeVolume(USD) | realizedCap(USD) | generatedCoins | fees | activeAddresses | averageDifficulty | paymentCount | medianTxValue(USD) | medianFee | blockSize | blockCount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-10-07 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 2011-10-08 | NaN | NaN | 0.0 | NaN | NaN | NaN | 0.0 | 50.0 | 0.0 | 1.0 | 0.000244 | 0.0 | NaN | NaN | 215.0 | 1.0 |
| 2 | 2011-10-09 | NaN | NaN | 0.0 | NaN | NaN | NaN | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | NaN | NaN | 0.0 | 0.0 |
| 3 | 2011-10-10 | NaN | NaN | 0.0 | NaN | NaN | NaN | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | NaN | NaN | 0.0 | 0.0 |
| 4 | 2011-10-11 | NaN | NaN | 0.0 | NaN | NaN | NaN | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | NaN | NaN | 0.0 | 0.0 |
There are 569 missing value.¶
In [5]:
dataset.isnull().sum()
Out[5]:
date 0 txVolume(USD) 569 adjustedTxVolume(USD) 569 txCount 1 marketcap(USD) 569 price(USD) 569 exchangeVolume(USD) 569 realizedCap(USD) 0 generatedCoins 4 fees 1 activeAddresses 1 averageDifficulty 4 paymentCount 1 medianTxValue(USD) 569 medianFee 6 blockSize 1 blockCount 1 dtype: int64
In [6]:
dataset.describe()
Out[6]:
| txVolume(USD) | adjustedTxVolume(USD) | txCount | marketcap(USD) | price(USD) | exchangeVolume(USD) | realizedCap(USD) | generatedCoins | fees | activeAddresses | averageDifficulty | paymentCount | medianTxValue(USD) | medianFee | blockSize | blockCount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.175000e+03 | 2.175000e+03 | 2743.000000 | 2.175000e+03 | 2175.000000 | 2.175000e+03 | 2.744000e+03 | 2740.000000 | 2743.000000 | 2743.000000 | 2.740000e+03 | 2.743000e+03 | 2175.000000 | 2738.000000 | 2.743000e+03 | 2743.000000 |
| mean | 2.194791e+08 | 6.855580e+07 | 11355.643821 | 1.696205e+09 | 31.892018 | 2.033175e+08 | 1.367312e+09 | 22379.715596 | 55.693079 | 35086.993802 | 1.420050e+06 | 2.827418e+04 | 173.553970 | 0.004611 | 7.024114e+06 | 588.004375 |
| std | 6.439949e+08 | 1.849340e+08 | 18226.614984 | 2.957302e+09 | 52.805824 | 4.867494e+08 | 2.306636e+09 | 12075.061827 | 96.677048 | 53077.073534 | 3.024695e+06 | 7.237327e+04 | 277.453356 | 0.018874 | 9.982084e+06 | 191.230663 |
| min | 4.484505e+05 | 3.792622e+05 | 0.000000 | 3.801208e+07 | 1.150000 | 0.000000e+00 | 0.000000e+00 | 50.000000 | 0.000000 | 0.000000 | 2.441406e-04 | 0.000000e+00 | 0.001760 | 0.000000 | 0.000000e+00 | 0.000000 |
| 25% | 7.930029e+06 | 3.696041e+06 | 2972.500000 | 1.349529e+08 | 3.355000 | 1.643170e+06 | 4.132866e+07 | 14550.000000 | 14.972662 | 8910.500000 | 9.575075e+02 | 6.146500e+03 | 41.363134 | 0.000224 | 2.150187e+06 | 554.000000 |
| 50% | 3.007672e+07 | 1.086806e+07 | 4503.000000 | 1.985032e+08 | 4.640000 | 4.154880e+06 | 2.801397e+08 | 19800.000000 | 25.982844 | 14007.000000 | 4.491421e+04 | 1.099600e+04 | 87.967825 | 0.001000 | 3.384537e+06 | 583.000000 |
| 75% | 1.265453e+08 | 4.638918e+07 | 14886.000000 | 2.394446e+09 | 44.100000 | 2.595530e+08 | 6.537123e+08 | 29300.000000 | 56.499317 | 42607.000000 | 2.294769e+05 | 3.104450e+04 | 219.318384 | 0.001000 | 8.746815e+06 | 613.000000 |
| max | 1.175670e+10 | 2.535597e+09 | 225860.000000 | 1.953073e+10 | 359.130000 | 6.961680e+09 | 7.313980e+09 | 484100.000000 | 1283.861162 | 607832.000000 | 1.187950e+07 | 1.859479e+06 | 4586.266455 | 0.100000 | 1.235592e+08 | 9682.000000 |
In [7]:
import seaborn as sns
plt.figure(figsize =(10, 10))
corr = dataset.corr()
sns.heatmap(corr, xticklabels=corr.columns.values ,yticklabels=corr.columns.values)
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-7-92a6fbe91511> in <module> ----> 1 import seaborn as sns 2 plt.figure(figsize =(10, 10)) 3 corr = dataset.corr() 4 sns.heatmap(corr, xticklabels=corr.columns.values ,yticklabels=corr.columns.values) ModuleNotFoundError: No module named 'seaborn'
Cleaning data¶
Dealing with missing data¶
In [8]:
#drop row 1~570
dataset.drop(dataset.index[:570], inplace=True)
print(dataset.shape)
dataset.isnull().sum()
(2174, 17)
Out[8]:
date 0 txVolume(USD) 0 adjustedTxVolume(USD) 0 txCount 0 marketcap(USD) 0 price(USD) 0 exchangeVolume(USD) 0 realizedCap(USD) 0 generatedCoins 0 fees 0 activeAddresses 0 averageDifficulty 0 paymentCount 0 medianTxValue(USD) 0 medianFee 0 blockSize 0 blockCount 0 dtype: int64
Drop original index and add new index start from 0¶
In [9]:
dataset= dataset.reset_index(drop=True)
In [10]:
dataset.head(5)
Out[10]:
| date | txVolume(USD) | adjustedTxVolume(USD) | txCount | marketcap(USD) | price(USD) | exchangeVolume(USD) | realizedCap(USD) | generatedCoins | fees | activeAddresses | averageDifficulty | paymentCount | medianTxValue(USD) | medianFee | blockSize | blockCount | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2013-04-29 | 3.647810e+07 | 2.111820e+06 | 9275.0 | 7.521684e+07 | 4.37 | 0.0 | 2.284118e+07 | 32500.0000 | 634.409741 | 18395.0 | 437.937821 | 9542.0 | 181.679890 | 0.10 | 4977931.0 | 650.0 |
| 1 | 2013-04-30 | 4.039166e+07 | 1.969543e+06 | 9099.0 | 7.574233e+07 | 4.40 | 0.0 | 2.319259e+07 | 31350.0000 | 792.170373 | 17810.0 | 437.937821 | 9301.0 | 167.863647 | 0.10 | 5349282.0 | 627.0 |
| 2 | 2013-05-01 | 7.637420e+07 | 4.691922e+06 | 8990.0 | 7.406414e+07 | 4.29 | 0.0 | 2.458101e+07 | 31699.9795 | 639.972367 | 16991.0 | 471.122764 | 9326.0 | 177.125873 | 0.10 | 4463820.0 | 634.0 |
| 3 | 2013-05-02 | 1.163151e+07 | 2.501720e+06 | 8031.0 | 6.537939e+07 | 3.78 | 0.0 | 2.479254e+07 | 26150.0000 | 528.803594 | 15769.0 | 482.512511 | 8269.0 | 155.490420 | 0.10 | 4088911.0 | 523.0 |
| 4 | 2013-05-03 | 4.632241e+06 | 1.964664e+06 | 6280.0 | 5.876169e+07 | 3.39 | 0.0 | 2.475425e+07 | 19900.0000 | 375.729091 | 12956.0 | 482.512511 | 6519.0 | 105.126185 | 0.05 | 3382172.0 | 398.0 |
In [11]:
dataset = dataset.sort_index(axis=1 ,ascending=True)
dataset = dataset.iloc[::-1]
dataset = dataset.sort_index(ascending=True, axis=0)
dataset = dataset.reindex(index = dataset.index[::-1])
dataset = dataset.reset_index()
dataset=dataset.drop('index', axis=1)
dataset.head(5)
Out[11]:
| activeAddresses | adjustedTxVolume(USD) | averageDifficulty | blockCount | blockSize | date | exchangeVolume(USD) | fees | generatedCoins | marketcap(USD) | medianFee | medianTxValue(USD) | paymentCount | price(USD) | realizedCap(USD) | txCount | txVolume(USD) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 102834.0 | 2.727544e+08 | 1.181510e+07 | 573.0 | 65641701.0 | 2019-04-11 | 3.461536e+09 | 17.753439 | 14325.0 | 5.418036e+09 | 0.000221 | 75.837923 | 75246.0 | 88.39 | 4.632666e+09 | 26512.0 | 5.026659e+08 |
| 1 | 99837.0 | 9.136417e+07 | 1.187950e+07 | 557.0 | 53092124.0 | 2019-04-10 | 2.765901e+09 | 16.292335 | 13925.0 | 5.317491e+09 | 0.000222 | 66.125885 | 76006.0 | 86.77 | 4.621039e+09 | 25088.0 | 1.995761e+08 |
| 2 | 70669.0 | 8.834103e+07 | 1.187950e+07 | 537.0 | 14846398.0 | 2019-04-09 | 2.742631e+09 | 16.982480 | 13425.0 | 5.483492e+09 | 0.000222 | 80.628978 | 46219.0 | 89.50 | 4.615411e+09 | 23994.0 | 1.891868e+08 |
| 3 | 140714.0 | 9.404545e+07 | 1.176827e+07 | 587.0 | 123559210.0 | 2019-04-08 | 3.295696e+09 | 18.271835 | 14675.0 | 5.656034e+09 | 0.000226 | 78.122568 | 50448.0 | 92.33 | 4.607194e+09 | 25967.0 | 2.400780e+08 |
| 4 | 162384.0 | 5.918720e+07 | 1.141644e+07 | 611.0 | 64268502.0 | 2019-04-07 | 3.314849e+09 | 15.560253 | 15275.0 | 5.660144e+09 | 0.000226 | 69.606270 | 138655.0 | 92.42 | 4.602690e+09 | 24003.0 | 1.881442e+08 |
In [12]:
dataset.shape
Out[12]:
(2174, 17)
In [13]:
py.init_notebook_mode()
btc_trace = go.Scatter(x=dataset['date'], y=dataset['price(USD)'], name= 'Price')
py.iplot([btc_trace])
Drop date¶
In [14]:
dataset=dataset.drop('date', axis=1)
In [15]:
dataset.head(5)
Out[15]:
| activeAddresses | adjustedTxVolume(USD) | averageDifficulty | blockCount | blockSize | exchangeVolume(USD) | fees | generatedCoins | marketcap(USD) | medianFee | medianTxValue(USD) | paymentCount | price(USD) | realizedCap(USD) | txCount | txVolume(USD) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 102834.0 | 2.727544e+08 | 1.181510e+07 | 573.0 | 65641701.0 | 3.461536e+09 | 17.753439 | 14325.0 | 5.418036e+09 | 0.000221 | 75.837923 | 75246.0 | 88.39 | 4.632666e+09 | 26512.0 | 5.026659e+08 |
| 1 | 99837.0 | 9.136417e+07 | 1.187950e+07 | 557.0 | 53092124.0 | 2.765901e+09 | 16.292335 | 13925.0 | 5.317491e+09 | 0.000222 | 66.125885 | 76006.0 | 86.77 | 4.621039e+09 | 25088.0 | 1.995761e+08 |
| 2 | 70669.0 | 8.834103e+07 | 1.187950e+07 | 537.0 | 14846398.0 | 2.742631e+09 | 16.982480 | 13425.0 | 5.483492e+09 | 0.000222 | 80.628978 | 46219.0 | 89.50 | 4.615411e+09 | 23994.0 | 1.891868e+08 |
| 3 | 140714.0 | 9.404545e+07 | 1.176827e+07 | 587.0 | 123559210.0 | 3.295696e+09 | 18.271835 | 14675.0 | 5.656034e+09 | 0.000226 | 78.122568 | 50448.0 | 92.33 | 4.607194e+09 | 25967.0 | 2.400780e+08 |
| 4 | 162384.0 | 5.918720e+07 | 1.141644e+07 | 611.0 | 64268502.0 | 3.314849e+09 | 15.560253 | 15275.0 | 5.660144e+09 | 0.000226 | 69.606270 | 138655.0 | 92.42 | 4.602690e+09 | 24003.0 | 1.881442e+08 |
In [16]:
print(type(dataset),"Data shape :",dataset.shape)
<class 'pandas.core.frame.DataFrame'> Data shape : (2174, 16)
In [17]:
dataset.shape
Out[17]:
(2174, 16)
Training Set.¶
In [18]:
len_day = 60
train_set = dataset.iloc[0:len(dataset)-len_day,:]
In [19]:
print(train_set.shape)
(2114, 16)
In [20]:
#save this data for the graph
X_train_4_graph = train_set['price(USD)']
In [21]:
X_train_4_graph= np.array(X_train_4_graph)
MinMax¶
In [22]:
from sklearn.preprocessing import MinMaxScaler
trainMinMax = MinMaxScaler()
train_set = trainMinMax.fit_transform(train_set)
In [23]:
train_set
Out[23]:
array([[1.59425211e-01, 1.07436574e-01, 9.94578482e-01, ...,
6.31694873e-01, 1.08887965e-01, 4.27191948e-02],
[1.54436671e-01, 3.58883943e-02, 1.00000000e+00, ...,
6.30097832e-01, 1.02522496e-01, 1.69380282e-02],
[1.05886211e-01, 3.46959389e-02, 1.00000000e+00, ...,
6.29324742e-01, 9.76321707e-02, 1.60543006e-02],
...,
[4.89532722e-03, 3.02233266e-04, 8.35279752e-07, ...,
3.34344798e-05, 7.15668263e-03, 3.18461602e-04],
[4.62234739e-03, 3.80901282e-04, 8.35279752e-07, ...,
1.04332519e-05, 7.83167268e-03, 3.09342103e-04],
[6.64972194e-03, 5.69653409e-04, 0.00000000e+00, ...,
0.00000000e+00, 9.22635411e-03, 3.61063488e-04]])
In [24]:
y_train_4G= train_set[:,12]
y_train = y_train_4G.tolist()
In [25]:
#train_set=train_set.drop(train_set[:,12], axis=1)
#trainset =np.delete(train_set, train_set[:,12])
X_train_4Nor=train_set[:,(0,1,2,3,4,5,6,7,8,9,10,11,13,14,15)]
#X_train=train_set
X_train_3D = X_train_4Nor.tolist()
In [26]:
print (len(X_train_3D))
2114
<h1 style =color:red;"> Important change 2D to 3D array</h1>
-Samples. One sequence is one sample. A batch is comprised of one or more samples.
-Time Steps. One time step is one point of observation in the sample.
-Features. One feature is one observation at a time step.
In [27]:
#X_train = X_train.tolist
X_train_3D = np.reshape(X_train_3D, (2114, 15, 1))
Test Set¶
In [28]:
test_set = dataset.iloc[:60,:]
print (test_set.shape)
print (test_set.head(5))
(60, 16)
activeAddresses adjustedTxVolume(USD) averageDifficulty blockCount \
0 102834.0 2.727544e+08 1.181510e+07 573.0
1 99837.0 9.136417e+07 1.187950e+07 557.0
2 70669.0 8.834103e+07 1.187950e+07 537.0
3 140714.0 9.404545e+07 1.176827e+07 587.0
4 162384.0 5.918720e+07 1.141644e+07 611.0
blockSize exchangeVolume(USD) fees generatedCoins \
0 65641701.0 3.461536e+09 17.753439 14325.0
1 53092124.0 2.765901e+09 16.292335 13925.0
2 14846398.0 2.742631e+09 16.982480 13425.0
3 123559210.0 3.295696e+09 18.271835 14675.0
4 64268502.0 3.314849e+09 15.560253 15275.0
marketcap(USD) medianFee medianTxValue(USD) paymentCount price(USD) \
0 5.418036e+09 0.000221 75.837923 75246.0 88.39
1 5.317491e+09 0.000222 66.125885 76006.0 86.77
2 5.483492e+09 0.000222 80.628978 46219.0 89.50
3 5.656034e+09 0.000226 78.122568 50448.0 92.33
4 5.660144e+09 0.000226 69.606270 138655.0 92.42
realizedCap(USD) txCount txVolume(USD)
0 4.632666e+09 26512.0 5.026659e+08
1 4.621039e+09 25088.0 1.995761e+08
2 4.615411e+09 23994.0 1.891868e+08
3 4.607194e+09 25967.0 2.400780e+08
4 4.602690e+09 24003.0 1.881442e+08
In [29]:
y_test_4_graph = test_set['price(USD)']
y_test_4_graph= y_test_4_graph.reset_index(drop=True)
In [30]:
from sklearn.preprocessing import MinMaxScaler
testMinMax = MinMaxScaler()
test_set = testMinMax.fit_transform(test_set)#D
In [31]:
y_test = test_set[:,12]
y_test = y_test.tolist()
In [32]:
X_test_4N=test_set[:,(0,1,2,3,4,5,6,7,8,9,10,11,13,14,15)]
#X_train=train_set
#X_train = X_train.values
#X_train = sc.fit_transform(X_train)
X_test_3D = X_test_4N.tolist()
In [33]:
print (len(X_test_3D))
60
In [34]:
X_test_3D = np.reshape(X_test_3D, (60, 15, 1))
In [35]:
print (y_test)
[0.9206380464749901, 0.8887357227254824, 0.9424970460811344, 0.998227648680583, 1.0, 0.9269397400551398, 0.8546671918077986, 0.8572272548247342, 0.681764474202442, 0.37534462386766454, 0.37672311933832225, 0.37416305632138647, 0.3861756597085466, 0.38460023631350926, 0.4052776683733754, 0.3499409216226861, 0.35191020086648295, 0.3674675068924773, 0.385978731784167, 0.3574241827491138, 0.3477747144545096, 0.3786923985821189, 0.37495076801890503, 0.36589208349743996, 0.38676644348168576, 0.3985821189444665, 0.3416699487987398, 0.2934226073257187, 0.2800315084679008, 0.30405671524222133, 0.27116975187081516, 0.30622292241039784, 0.32414336352894835, 0.28278849940921624, 0.31114612051988977, 0.27963765261914153, 0.22410397794407255, 0.09610082709728252, 0.1317447814100039, 0.14533280819220173, 0.11500590783773135, 0.09058684521465143, 0.07837731390311142, 0.07601417881055539, 0.08940527766837336, 0.05789680976762501, 0.19771563607719567, 0.15813312327688067, 0.14749901536037813, 0.2008664828672706, 0.12170145726664039, 0.12800315084679015, 0.044111855061047756, 0.03465931469082317, 0.022055927530523767, 0.0, 0.004726270185112291, 0.043127215439149214, 0.030129972430090635, 0.10319023237495084]
Loss function¶
Mean Squared Error formula¶
LSTM is made up of three gates:
Forget Gate (f_t) - Controls if/when the context is forgotten. (MC)
Input Gate (i_t) - Controls if/when a value should be remembered by the context. (M+/MS)
Output Gate (o_t) - Controls if/when the remembered value is allowed to pass from the unit. (RM)
Sigmoid function.¶
Gradient descent optimization algorithms¶
Stochastic gradient descent optimizer
RMSProp optimizer
Adagrad optimizer
Adadelta opimizer
Adamax, and Nadam
Adaptive Moment Estimation (Adam)
Two layer with Sigmoid and RELU activation fuction¶
In [36]:
sigRulu = Sequential()
#Adding the input layer and the LSTM layer
sigRulu.add(LSTM(units = 50, activation = 'sigmoid',return_sequences=True, input_shape = (None, 1)))
#regressor.add(LSTM(units=20,return_sequences=True))
#Adding the output layer
sigRulu.add(LSTM(units = 100, activation= 'relu', return_sequences=False))
sigRulu.add(Dropout(rate=.2))
sigRulu.add(Dense(units = 1))
#Compiling the Recurrent Neural Network
sigRulu.compile(optimizer = 'adam', loss = 'mean_squared_error')
WARNING:tensorflow:From C:\Users\gustl\Anaconda3\envs\Deeplearning\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From C:\Users\gustl\Anaconda3\envs\Deeplearning\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
In [37]:
historyTwo= sigRulu.fit(X_train_3D, y_train, batch_size = 50, epochs = 500)
WARNING:tensorflow:From C:\Users\gustl\Anaconda3\envs\Deeplearning\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. Epoch 1/500 2114/2114 [==============================] - 5s 2ms/step - loss: 0.0273 Epoch 2/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0225 Epoch 3/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0221 Epoch 4/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0215A: 0s - loss: 0.02 Epoch 5/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0213 Epoch 6/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0203 Epoch 7/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0191 Epoch 8/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0125 Epoch 9/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0103 Epoch 10/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0072 Epoch 11/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0071A: 0s - l Epoch 12/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0061A: 0s - l Epoch 13/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0054 Epoch 14/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0052 Epoch 15/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0050 Epoch 16/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0039A: 0s - loss Epoch 17/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0062 Epoch 18/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0049 Epoch 19/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0039 Epoch 20/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0038 Epoch 21/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0038 Epoch 22/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0042 Epoch 23/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0038 Epoch 24/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0035 Epoch 25/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0038 Epoch 26/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0030A: 0s - loss: 0.00 Epoch 27/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0033 Epoch 28/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0031 Epoch 29/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0026 Epoch 30/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0027 Epoch 31/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0030 Epoch 32/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0027 Epoch 33/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0024 Epoch 34/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0027 Epoch 35/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0034 Epoch 36/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0029A: 0s - los Epoch 37/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0023 Epoch 38/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0024 Epoch 39/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0028 Epoch 40/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0026 Epoch 41/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0025 Epoch 42/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0024 Epoch 43/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0023 Epoch 44/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0026 Epoch 45/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0020 Epoch 46/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0019 Epoch 47/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0021 Epoch 48/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0023 Epoch 49/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0021 Epoch 50/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0021 Epoch 51/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0020 Epoch 52/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0018 Epoch 53/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0018 Epoch 54/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0021A: 0s - Epoch 55/500 2114/2114 [==============================] - 5s 2ms/step - loss: 0.0019 Epoch 56/500 2114/2114 [==============================] - 8s 4ms/step - loss: 0.0022 Epoch 57/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0019 Epoch 58/500 2114/2114 [==============================] - 5s 2ms/step - loss: 0.0020 Epoch 59/500 2114/2114 [==============================] - 7s 3ms/step - loss: 0.0021 Epoch 60/500 2114/2114 [==============================] - 5s 2ms/step - loss: 0.0018 Epoch 61/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0016 Epoch 62/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0019 Epoch 63/500 2114/2114 [==============================] - 2s 750us/step - loss: 0.0018 Epoch 64/500 2114/2114 [==============================] - 1s 702us/step - loss: 0.0017 Epoch 65/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0018 Epoch 66/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0014 Epoch 67/500 2114/2114 [==============================] - 2s 943us/step - loss: 0.0016 Epoch 68/500 2114/2114 [==============================] - 2s 891us/step - loss: 0.0015 Epoch 69/500 2114/2114 [==============================] - 2s 894us/step - loss: 0.0016 Epoch 70/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0018 Epoch 71/500 2114/2114 [==============================] - 1s 643us/step - loss: 0.0018 Epoch 72/500 2114/2114 [==============================] - 1s 699us/step - loss: 0.0016 Epoch 73/500 2114/2114 [==============================] - 1s 645us/step - loss: 0.0016 Epoch 74/500 2114/2114 [==============================] - 1s 703us/step - loss: 0.0014 Epoch 75/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0016 Epoch 76/500 2114/2114 [==============================] - 2s 883us/step - loss: 0.0014 Epoch 77/500 2114/2114 [==============================] - 2s 874us/step - loss: 0.0014 Epoch 78/500 2114/2114 [==============================] - 2s 849us/step - loss: 0.0018 Epoch 79/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0014 Epoch 80/500 2114/2114 [==============================] - 2s 919us/step - loss: 0.0015 Epoch 81/500 2114/2114 [==============================] - 2s 940us/step - loss: 0.0013 Epoch 82/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0016 Epoch 83/500 2114/2114 [==============================] - 2s 996us/step - loss: 0.0014 Epoch 84/500 2114/2114 [==============================] - 2s 893us/step - loss: 0.0012 Epoch 85/500 2114/2114 [==============================] - 2s 897us/step - loss: 0.0014 Epoch 86/500 2114/2114 [==============================] - 2s 864us/step - loss: 0.0014 Epoch 87/500 2114/2114 [==============================] - 2s 848us/step - loss: 0.0014 Epoch 88/500 2114/2114 [==============================] - 2s 937us/step - loss: 0.0014 Epoch 89/500 2114/2114 [==============================] - 2s 852us/step - loss: 0.0014 Epoch 90/500 2114/2114 [==============================] - 2s 762us/step - loss: 0.0017 Epoch 91/500 2114/2114 [==============================] - 1s 623us/step - loss: 0.0014 Epoch 92/500 2114/2114 [==============================] - 1s 668us/step - loss: 0.0011 0s - loss: 0 Epoch 93/500 2114/2114 [==============================] - 1s 598us/step - loss: 0.0012 0s - Epoch 94/500 2114/2114 [==============================] - 1s 640us/step - loss: 0.0013 Epoch 95/500 2114/2114 [==============================] - 1s 656us/step - loss: 0.0013 0s - l Epoch 96/500 2114/2114 [==============================] - 2s 930us/step - loss: 0.0014 Epoch 97/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0013 Epoch 98/500 2114/2114 [==============================] - 2s 726us/step - loss: 0.0014 Epoch 99/500 2114/2114 [==============================] - 2s 855us/step - loss: 0.0014 Epoch 100/500 2114/2114 [==============================] - 2s 835us/step - loss: 0.0013 Epoch 101/500 2114/2114 [==============================] - 2s 865us/step - loss: 0.0012 Epoch 102/500 2114/2114 [==============================] - 2s 860us/step - loss: 0.0014 Epoch 103/500 2114/2114 [==============================] - 2s 849us/step - loss: 0.0011 Epoch 104/500 2114/2114 [==============================] - 2s 844us/step - loss: 0.0015 Epoch 105/500 2114/2114 [==============================] - 2s 866us/step - loss: 0.0015 Epoch 106/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0012 Epoch 107/500 2114/2114 [==============================] - 2s 867us/step - loss: 0.0015 Epoch 108/500 2114/2114 [==============================] - 2s 878us/step - loss: 0.0012 Epoch 109/500 2114/2114 [==============================] - 2s 901us/step - loss: 0.0012 Epoch 110/500 2114/2114 [==============================] - 2s 835us/step - loss: 0.0012 Epoch 111/500 2114/2114 [==============================] - 1s 605us/step - loss: 0.0014 Epoch 112/500 2114/2114 [==============================] - 1s 684us/step - loss: 0.0012 Epoch 113/500 2114/2114 [==============================] - 2s 896us/step - loss: 9.9960e-04 Epoch 114/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0011- ETA Epoch 115/500 2114/2114 [==============================] - 2s 1ms/step - loss: 9.9821e-04 Epoch 116/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0012 Epoch 117/500 2114/2114 [==============================] - 2s 952us/step - loss: 0.0012 Epoch 118/500 2114/2114 [==============================] - 1s 703us/step - loss: 0.0015 Epoch 119/500 2114/2114 [==============================] - 1s 635us/step - loss: 0.0012 Epoch 120/500 2114/2114 [==============================] - 1s 612us/step - loss: 0.0010 Epoch 121/500 2114/2114 [==============================] - 1s 609us/step - loss: 0.0012 Epoch 122/500 2114/2114 [==============================] - 1s 680us/step - loss: 0.0010 Epoch 123/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0010A: 0s - loss: 0 Epoch 124/500 2114/2114 [==============================] - 2s 940us/step - loss: 0.0011 Epoch 125/500 2114/2114 [==============================] - 2s 821us/step - loss: 0.0012 Epoch 126/500 2114/2114 [==============================] - 2s 721us/step - loss: 0.0015 Epoch 127/500 2114/2114 [==============================] - 2s 747us/step - loss: 0.0011 Epoch 128/500 2114/2114 [==============================] - 2s 747us/step - loss: 0.0011 Epoch 129/500 2114/2114 [==============================] - 2s 749us/step - loss: 0.0011 Epoch 130/500 2114/2114 [==============================] - 2s 717us/step - loss: 0.0010 Epoch 131/500 2114/2114 [==============================] - 2s 752us/step - loss: 0.0011 Epoch 132/500 2114/2114 [==============================] - 2s 722us/step - loss: 8.9259e-04 Epoch 133/500 2114/2114 [==============================] - 2s 909us/step - loss: 0.0010 Epoch 134/500 2114/2114 [==============================] - 1s 657us/step - loss: 0.0012 Epoch 135/500 2114/2114 [==============================] - 2s 746us/step - loss: 0.0010 Epoch 136/500 2114/2114 [==============================] - 2s 729us/step - loss: 9.0086e-04 Epoch 137/500 2114/2114 [==============================] - 2s 764us/step - loss: 0.0011 Epoch 138/500 2114/2114 [==============================] - 1s 677us/step - loss: 0.0011 Epoch 139/500 2114/2114 [==============================] - 1s 628us/step - loss: 0.0011 Epoch 140/500 2114/2114 [==============================] - 1s 620us/step - loss: 8.0639e-04 Epoch 141/500 2114/2114 [==============================] - 1s 647us/step - loss: 0.0011 Epoch 142/500 2114/2114 [==============================] - 1s 615us/step - loss: 0.0011 0s - l Epoch 143/500 2114/2114 [==============================] - 1s 612us/step - loss: 0.0011 Epoch 144/500 2114/2114 [==============================] - 2s 942us/step - loss: 0.0012 Epoch 145/500 2114/2114 [==============================] - 2s 777us/step - loss: 0.0010 Epoch 146/500 2114/2114 [==============================] - 2s 742us/step - loss: 9.3139e-04 Epoch 147/500 2114/2114 [==============================] - 2s 774us/step - loss: 8.2794e-04 Epoch 148/500 2114/2114 [==============================] - 2s 728us/step - loss: 9.8824e-04 Epoch 149/500 2114/2114 [==============================] - 1s 708us/step - loss: 0.0013 Epoch 150/500 2114/2114 [==============================] - 1s 685us/step - loss: 8.9507e-04 Epoch 151/500 2114/2114 [==============================] - 1s 709us/step - loss: 8.7181e-04 Epoch 152/500 2114/2114 [==============================] - 2s 760us/step - loss: 0.0010 Epoch 153/500 2114/2114 [==============================] - 2s 721us/step - loss: 0.0011 Epoch 154/500 2114/2114 [==============================] - 2s 981us/step - loss: 0.0012 Epoch 155/500 2114/2114 [==============================] - 2s 753us/step - loss: 0.0012 Epoch 156/500 2114/2114 [==============================] - 2s 797us/step - loss: 9.2299e-04 Epoch 157/500 2114/2114 [==============================] - 2s 729us/step - loss: 9.7340e-04 Epoch 158/500 2114/2114 [==============================] - 1s 682us/step - loss: 9.7399e-04 Epoch 159/500 2114/2114 [==============================] - 1s 704us/step - loss: 8.2802e-04 Epoch 160/500 2114/2114 [==============================] - 1s 681us/step - loss: 8.1916e-04 Epoch 161/500 2114/2114 [==============================] - 1s 680us/step - loss: 0.0011 Epoch 162/500 2114/2114 [==============================] - 1s 626us/step - loss: 0.0010 Epoch 163/500 2114/2114 [==============================] - 1s 615us/step - loss: 9.4580e-04 Epoch 164/500 2114/2114 [==============================] - 2s 789us/step - loss: 9.9404e-04 Epoch 165/500 2114/2114 [==============================] - 1s 641us/step - loss: 7.3200e-04 Epoch 166/500 2114/2114 [==============================] - 1s 626us/step - loss: 0.0011 Epoch 167/500 2114/2114 [==============================] - 1s 623us/step - loss: 0.0010 Epoch 168/500 2114/2114 [==============================] - 1s 640us/step - loss: 9.0563e-04 Epoch 169/500 2114/2114 [==============================] - 1s 643us/step - loss: 7.6261e-04 Epoch 170/500 2114/2114 [==============================] - 1s 617us/step - loss: 9.8892e-04 Epoch 171/500 2114/2114 [==============================] - 1s 667us/step - loss: 0.0012 Epoch 172/500 2114/2114 [==============================] - 2s 715us/step - loss: 7.8527e-04 Epoch 173/500 2114/2114 [==============================] - 2s 771us/step - loss: 8.8936e-04 Epoch 174/500 2114/2114 [==============================] - 2s 960us/step - loss: 8.1204e-04 Epoch 175/500 2114/2114 [==============================] - 2s 984us/step - loss: 7.3187e-04 Epoch 176/500 2114/2114 [==============================] - 2s 781us/step - loss: 8.4684e-04 Epoch 177/500 2114/2114 [==============================] - 2s 733us/step - loss: 9.5787e-04 Epoch 178/500 2114/2114 [==============================] - ETA: 0s - loss: 8.6552e-0 - 2s 777us/step - loss: 8.6162e-04 Epoch 179/500 2114/2114 [==============================] - 2s 924us/step - loss: 7.4668e-04 Epoch 180/500 2114/2114 [==============================] - 2s 745us/step - loss: 9.3215e-04 Epoch 181/500 2114/2114 [==============================] - 1s 699us/step - loss: 8.7018e-04 Epoch 182/500 2114/2114 [==============================] - 1s 675us/step - loss: 7.9231e-04 Epoch 183/500 2114/2114 [==============================] - 1s 608us/step - loss: 9.6376e-04 Epoch 184/500 2114/2114 [==============================] - 1s 599us/step - loss: 0.0012 Epoch 185/500 2114/2114 [==============================] - 2s 892us/step - loss: 0.0010 0s - loss: Epoch 186/500 2114/2114 [==============================] - 2s 740us/step - loss: 7.7623e-04 Epoch 187/500 2114/2114 [==============================] - 2s 747us/step - loss: 6.5012e-04 Epoch 188/500 2114/2114 [==============================] - 2s 750us/step - loss: 9.1309e-04 Epoch 189/500 2114/2114 [==============================] - 2s 778us/step - loss: 0.0011 Epoch 190/500 2114/2114 [==============================] - 2s 744us/step - loss: 8.8935e-04 Epoch 191/500 2114/2114 [==============================] - 2s 774us/step - loss: 7.4841e-04 Epoch 192/500 2114/2114 [==============================] - 2s 731us/step - loss: 8.2450e-04 Epoch 193/500 2114/2114 [==============================] - 2s 756us/step - loss: 7.6809e-04 Epoch 194/500 2114/2114 [==============================] - 2s 750us/step - loss: 9.7036e-04 Epoch 195/500 2114/2114 [==============================] - 2s 955us/step - loss: 7.6339e-04 Epoch 196/500 2114/2114 [==============================] - 2s 779us/step - loss: 8.6418e-04 Epoch 197/500 2114/2114 [==============================] - 2s 748us/step - loss: 7.0891e-04 Epoch 198/500 2114/2114 [==============================] - 2s 782us/step - loss: 8.3600e-04 Epoch 199/500 2114/2114 [==============================] - 2s 735us/step - loss: 8.6136e-04 Epoch 200/500 2114/2114 [==============================] - 2s 761us/step - loss: 8.2144e-04 Epoch 201/500 2114/2114 [==============================] - 2s 895us/step - loss: 7.0822e-04 Epoch 202/500 2114/2114 [==============================] - 2s 752us/step - loss: 7.8139e-04 Epoch 203/500 2114/2114 [==============================] - 1s 646us/step - loss: 7.7632e-04 Epoch 204/500 2114/2114 [==============================] - 2s 741us/step - loss: 9.4438e-04 Epoch 205/500 2114/2114 [==============================] - 2s 862us/step - loss: 7.4195e-04 Epoch 206/500 2114/2114 [==============================] - 1s 636us/step - loss: 7.4429e-04 Epoch 207/500 2114/2114 [==============================] - 1s 641us/step - loss: 7.4546e-04 Epoch 208/500 2114/2114 [==============================] - 1s 672us/step - loss: 8.2858e-04 Epoch 209/500 2114/2114 [==============================] - 2s 771us/step - loss: 6.6167e-04 Epoch 210/500 2114/2114 [==============================] - 2s 750us/step - loss: 7.6839e-04 Epoch 211/500 2114/2114 [==============================] - 2s 743us/step - loss: 7.5645e-04 Epoch 212/500 2114/2114 [==============================] - 2s 724us/step - loss: 7.3018e-04 Epoch 213/500 2114/2114 [==============================] - 2s 752us/step - loss: 8.6711e-04 Epoch 214/500 2114/2114 [==============================] - 2s 866us/step - loss: 7.2123e-04 Epoch 215/500 2114/2114 [==============================] - 2s 1ms/step - loss: 6.8400e-04 Epoch 216/500 2114/2114 [==============================] - 2s 764us/step - loss: 6.7893e-04 Epoch 217/500 2114/2114 [==============================] - 2s 748us/step - loss: 7.2194e-04 Epoch 218/500 2114/2114 [==============================] - 2s 752us/step - loss: 7.6359e-04 Epoch 219/500 2114/2114 [==============================] - 2s 739us/step - loss: 8.0602e-04 Epoch 220/500 2114/2114 [==============================] - 2s 718us/step - loss: 7.1464e-04 Epoch 221/500 2114/2114 [==============================] - 1s 639us/step - loss: 7.6278e-04 Epoch 222/500 2114/2114 [==============================] - 1s 603us/step - loss: 8.3673e-04 Epoch 223/500 2114/2114 [==============================] - 1s 634us/step - loss: 7.0911e-04 Epoch 224/500 2114/2114 [==============================] - 1s 610us/step - loss: 6.3766e-04 Epoch 225/500 2114/2114 [==============================] - 2s 789us/step - loss: 7.4521e-04 Epoch 226/500 2114/2114 [==============================] - 1s 670us/step - loss: 6.6412e-04 Epoch 227/500 2114/2114 [==============================] - 1s 620us/step - loss: 7.9401e-04 1s Epoch 228/500 2114/2114 [==============================] - 1s 642us/step - loss: 7.0297e-04 Epoch 229/500 2114/2114 [==============================] - 1s 626us/step - loss: 7.1951e-04 Epoch 230/500 2114/2114 [==============================] - 1s 652us/step - loss: 0.0011 Epoch 231/500 2114/2114 [==============================] - 1s 671us/step - loss: 6.9370e-04 Epoch 232/500 2114/2114 [==============================] - 2s 722us/step - loss: 6.3560e-04 Epoch 233/500 2114/2114 [==============================] - 2s 784us/step - loss: 7.1803e-04 Epoch 234/500 2114/2114 [==============================] - 2s 751us/step - loss: 6.6786e-04 Epoch 235/500 2114/2114 [==============================] - 2s 775us/step - loss: 7.4447e-04 Epoch 236/500 2114/2114 [==============================] - 2s 1ms/step - loss: 7.0441e-04 Epoch 237/500 2114/2114 [==============================] - 2s 989us/step - loss: 7.0682e-04 Epoch 238/500 2114/2114 [==============================] - 2s 854us/step - loss: 5.7133e-04 Epoch 239/500 2114/2114 [==============================] - 6s 3ms/step - loss: 6.1675e-04 Epoch 240/500 2114/2114 [==============================] - 7s 3ms/step - loss: 7.7751e-04 Epoch 241/500 2114/2114 [==============================] - 4s 2ms/step - loss: 7.3284e-04 Epoch 242/500 2114/2114 [==============================] - 3s 2ms/step - loss: 8.2170e-04 Epoch 243/500 2114/2114 [==============================] - 3s 2ms/step - loss: 5.4496e-04 Epoch 244/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.9381e-04 Epoch 245/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.6142e-04 Epoch 246/500 2114/2114 [==============================] - 4s 2ms/step - loss: 7.4846e-04 Epoch 247/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.2210e-04 Epoch 248/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.7503e-04 Epoch 249/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.2241e-04 Epoch 250/500 2114/2114 [==============================] - 5s 2ms/step - loss: 6.6197e-04 Epoch 251/500 2114/2114 [==============================] - 3s 1ms/step - loss: 7.2795e-04 Epoch 252/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.9452e-04 Epoch 253/500 2114/2114 [==============================] - 3s 1ms/step - loss: 7.1044e-04 Epoch 254/500 2114/2114 [==============================] - 2s 822us/step - loss: 6.8541e-04 Epoch 255/500 2114/2114 [==============================] - 2s 756us/step - loss: 7.6790e-04 Epoch 256/500 2114/2114 [==============================] - 2s 796us/step - loss: 7.6198e-04 Epoch 257/500 2114/2114 [==============================] - 2s 811us/step - loss: 7.4339e-04 Epoch 258/500 2114/2114 [==============================] - 2s 773us/step - loss: 7.6611e-04 Epoch 259/500 2114/2114 [==============================] - 2s 729us/step - loss: 6.5987e-04 Epoch 260/500 2114/2114 [==============================] - 2s 911us/step - loss: 7.5603e-04 Epoch 261/500 2114/2114 [==============================] - 2s 819us/step - loss: 6.4439e-04 Epoch 262/500 2114/2114 [==============================] - 2s 780us/step - loss: 6.3695e-04 Epoch 263/500 2114/2114 [==============================] - 2s 764us/step - loss: 8.1259e-04 Epoch 264/500 2114/2114 [==============================] - 2s 725us/step - loss: 7.9445e-04 Epoch 265/500 2114/2114 [==============================] - 2s 790us/step - loss: 6.8279e-04 Epoch 266/500 2114/2114 [==============================] - 2s 920us/step - loss: 5.7711e-04 Epoch 267/500 2114/2114 [==============================] - 2s 757us/step - loss: 6.9081e-04 Epoch 268/500 2114/2114 [==============================] - 2s 719us/step - loss: 7.3989e-04 Epoch 269/500 2114/2114 [==============================] - 1s 658us/step - loss: 7.2062e-04 Epoch 270/500 2114/2114 [==============================] - 2s 862us/step - loss: 6.6513e-04 Epoch 271/500 2114/2114 [==============================] - 2s 786us/step - loss: 6.5107e-04 Epoch 272/500 2114/2114 [==============================] - 2s 908us/step - loss: 7.1964e-04 Epoch 273/500 2114/2114 [==============================] - 2s 1ms/step - loss: 7.1027e-04 Epoch 274/500 2114/2114 [==============================] - 2s 881us/step - loss: 5.8926e-04 Epoch 275/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.7295e-04 Epoch 276/500 2114/2114 [==============================] - 2s 945us/step - loss: 7.5125e-04 Epoch 277/500 2114/2114 [==============================] - 1s 631us/step - loss: 6.1076e-04 0s - lo Epoch 278/500 2114/2114 [==============================] - 1s 610us/step - loss: 7.5038e-04 Epoch 279/500 2114/2114 [==============================] - 2s 837us/step - loss: 6.2206e-04 0s - loss: 6.331 Epoch 280/500 2114/2114 [==============================] - 1s 624us/step - loss: 5.3866e-04 Epoch 281/500 2114/2114 [==============================] - 1s 618us/step - loss: 5.0907e-04 Epoch 282/500 2114/2114 [==============================] - 1s 638us/step - loss: 6.8338e-04 Epoch 283/500 2114/2114 [==============================] - 1s 603us/step - loss: 5.9045e-04 Epoch 284/500 2114/2114 [==============================] - 2s 723us/step - loss: 5.3552e-04 Epoch 285/500 2114/2114 [==============================] - 1s 550us/step - loss: 6.1503e-04 Epoch 286/500 2114/2114 [==============================] - 1s 591us/step - loss: 5.2869e-04 Epoch 287/500 2114/2114 [==============================] - 1s 600us/step - loss: 6.3775e-04 Epoch 288/500 2114/2114 [==============================] - 1s 666us/step - loss: 5.7331e-04 Epoch 289/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.3368e-04 Epoch 290/500 2114/2114 [==============================] - 2s 756us/step - loss: 6.5094e-04 Epoch 291/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.8767e-04 Epoch 292/500 2114/2114 [==============================] - 2s 1ms/step - loss: 6.6660e-04 Epoch 293/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.5010e-04 Epoch 294/500 2114/2114 [==============================] - 2s 1ms/step - loss: 5.3555e-04 Epoch 295/500 2114/2114 [==============================] - 2s 858us/step - loss: 6.5846e-04 Epoch 296/500 2114/2114 [==============================] - 2s 720us/step - loss: 6.2281e-04 Epoch 297/500 2114/2114 [==============================] - 2s 768us/step - loss: 5.8899e-04 Epoch 298/500 2114/2114 [==============================] - 1s 632us/step - loss: 6.2133e-04 Epoch 299/500 2114/2114 [==============================] - 1s 612us/step - loss: 6.1503e-04 Epoch 300/500 2114/2114 [==============================] - 1s 643us/step - loss: 5.8990e-04 Epoch 301/500 2114/2114 [==============================] - 1s 634us/step - loss: 5.7272e-04 Epoch 302/500 2114/2114 [==============================] - 2s 793us/step - loss: 5.7459e-04 0s - Epoch 303/500 2114/2114 [==============================] - 2s 1ms/step - loss: 7.2124e-04 Epoch 304/500 2114/2114 [==============================] - 2s 753us/step - loss: 6.4217e-04 Epoch 305/500 2114/2114 [==============================] - 2s 725us/step - loss: 6.7751e-04 Epoch 306/500 2114/2114 [==============================] - 2s 712us/step - loss: 6.0031e-04 Epoch 307/500 2114/2114 [==============================] - 2s 784us/step - loss: 6.0570e-04 Epoch 308/500 2114/2114 [==============================] - 1s 656us/step - loss: 6.3476e-04 Epoch 309/500 2114/2114 [==============================] - 1s 618us/step - loss: 6.3617e-04 0s - loss: 6. Epoch 310/500 2114/2114 [==============================] - 1s 643us/step - loss: 5.7726e-04 Epoch 311/500 2114/2114 [==============================] - 1s 606us/step - loss: 6.0358e-04 0s - loss: 6.0758e-0 Epoch 312/500 2114/2114 [==============================] - 1s 627us/step - loss: 6.2932e-04 Epoch 313/500 2114/2114 [==============================] - 1s 622us/step - loss: 5.5504e-04 Epoch 314/500 2114/2114 [==============================] - 1s 652us/step - loss: 7.3032e-04 Epoch 315/500 2114/2114 [==============================] - 1s 681us/step - loss: 6.0508e-04 Epoch 316/500 2114/2114 [==============================] - 1s 656us/step - loss: 5.8187e-04 Epoch 317/500 2114/2114 [==============================] - 1s 615us/step - loss: 6.9199e-04 Epoch 318/500 2114/2114 [==============================] - 1s 664us/step - loss: 5.9547e-04 Epoch 319/500 2114/2114 [==============================] - 2s 869us/step - loss: 5.7460e-04 Epoch 320/500 2114/2114 [==============================] - 1s 677us/step - loss: 6.8495e-04 Epoch 321/500 2114/2114 [==============================] - 1s 654us/step - loss: 7.1106e-04 0s - loss: Epoch 322/500 2114/2114 [==============================] - 2s 732us/step - loss: 6.1283e-04 0s - loss: Epoch 323/500 2114/2114 [==============================] - 1s 649us/step - loss: 5.7835e-04 Epoch 324/500 2114/2114 [==============================] - 1s 682us/step - loss: 5.1961e-04 Epoch 325/500 2114/2114 [==============================] - 1s 614us/step - loss: 6.0748e-04 0s - loss: 5 Epoch 326/500 2114/2114 [==============================] - 2s 757us/step - loss: 6.9119e-04 Epoch 327/500 2114/2114 [==============================] - 1s 599us/step - loss: 7.5606e-04 Epoch 328/500 2114/2114 [==============================] - 1s 604us/step - loss: 6.3497e-04 1s Epoch 329/500 2114/2114 [==============================] - 1s 612us/step - loss: 6.3854e-04 Epoch 330/500 2114/2114 [==============================] - 2s 845us/step - loss: 5.7380e-04 Epoch 331/500 2114/2114 [==============================] - 1s 634us/step - loss: 5.7344e-04 Epoch 332/500 2114/2114 [==============================] - 1s 619us/step - loss: 6.3637e-04 Epoch 333/500 2114/2114 [==============================] - 1s 612us/step - loss: 5.8521e-04 0s - loss: 6.8 - ETA: 0s - loss: Epoch 334/500 2114/2114 [==============================] - 1s 614us/step - loss: 5.5144e-04 Epoch 335/500 2114/2114 [==============================] - 1s 610us/step - loss: 5.4620e-04 Epoch 336/500 2114/2114 [==============================] - 1s 626us/step - loss: 6.7086e-04 1s - Epoch 337/500 2114/2114 [==============================] - 1s 633us/step - loss: 5.4323e-04 0s - loss: 5.4138e Epoch 338/500 2114/2114 [==============================] - 1s 610us/step - loss: 6.1880e-04 Epoch 339/500 2114/2114 [==============================] - 1s 629us/step - loss: 6.4150e-04 0s - loss Epoch 340/500 2114/2114 [==============================] - 1s 624us/step - loss: 6.7502e-04 Epoch 341/500 2114/2114 [==============================] - 1s 603us/step - loss: 5.4461e-04 Epoch 342/500 2114/2114 [==============================] - 2s 801us/step - loss: 6.0410e-04 Epoch 343/500 2114/2114 [==============================] - 1s 627us/step - loss: 7.5353e-04 Epoch 344/500 2114/2114 [==============================] - 1s 617us/step - loss: 7.1398e-04 Epoch 345/500 2114/2114 [==============================] - 1s 619us/step - loss: 5.8870e-04 Epoch 346/500 2114/2114 [==============================] - 1s 613us/step - loss: 5.4426e-04 0s - los Epoch 347/500 2114/2114 [==============================] - 1s 622us/step - loss: 6.5810e-04 0s - loss: Epoch 348/500 2114/2114 [==============================] - 1s 622us/step - loss: 6.9828e-04 Epoch 349/500 2114/2114 [==============================] - 1s 621us/step - loss: 6.0089e-04 Epoch 350/500 2114/2114 [==============================] - 1s 607us/step - loss: 6.8061e-04 Epoch 351/500 2114/2114 [==============================] - 1s 627us/step - loss: 5.8606e-04 Epoch 352/500 2114/2114 [==============================] - 1s 605us/step - loss: 6.2388e-04 Epoch 353/500 2114/2114 [==============================] - 1s 611us/step - loss: 5.5643e-04 Epoch 354/500 2114/2114 [==============================] - 2s 819us/step - loss: 5.9960e-04 Epoch 355/500 2114/2114 [==============================] - 1s 621us/step - loss: 6.4559e-04 Epoch 356/500 2114/2114 [==============================] - 1s 610us/step - loss: 6.5849e-04 Epoch 357/500 2114/2114 [==============================] - 1s 619us/step - loss: 5.0255e-04 Epoch 358/500 2114/2114 [==============================] - 1s 623us/step - loss: 5.7631e-04 Epoch 359/500 2114/2114 [==============================] - 1s 600us/step - loss: 5.9615e-04 Epoch 360/500 2114/2114 [==============================] - 1s 640us/step - loss: 7.3339e-04 Epoch 361/500 2114/2114 [==============================] - 1s 621us/step - loss: 8.0141e-04 0s Epoch 362/500 2114/2114 [==============================] - 1s 617us/step - loss: 7.3997e-04 Epoch 363/500 2114/2114 [==============================] - 1s 589us/step - loss: 7.1684e-04 Epoch 364/500 2114/2114 [==============================] - 1s 598us/step - loss: 8.1935e-04 Epoch 365/500 2114/2114 [==============================] - 1s 593us/step - loss: 6.8246e-04 Epoch 366/500 2114/2114 [==============================] - 2s 781us/step - loss: 5.4010e-04 Epoch 367/500 2114/2114 [==============================] - 1s 610us/step - loss: 6.4050e-04 Epoch 368/500 2114/2114 [==============================] - 1s 601us/step - loss: 6.6346e-04 Epoch 369/500 2114/2114 [==============================] - 1s 605us/step - loss: 6.2649e-04 Epoch 370/500 2114/2114 [==============================] - 1s 591us/step - loss: 5.7022e-04 Epoch 371/500 2114/2114 [==============================] - 1s 600us/step - loss: 6.0294e-04 0s - loss: 5.894 - ETA: 0s - loss: 5.9180 Epoch 372/500 2114/2114 [==============================] - 1s 618us/step - loss: 6.7088e-04 0s - loss: 6 - ETA: 0s - loss: 6.6501e- Epoch 373/500 2114/2114 [==============================] - 1s 595us/step - loss: 6.5280e-04 Epoch 374/500 2114/2114 [==============================] - 1s 612us/step - loss: 6.4485e-04 Epoch 375/500 2114/2114 [==============================] - 1s 615us/step - loss: 5.6764e-04 Epoch 376/500 2114/2114 [==============================] - 1s 603us/step - loss: 5.4628e-04 0s - loss: 5.6061e Epoch 377/500 2114/2114 [==============================] - 1s 596us/step - loss: 5.7063e-04 Epoch 378/500 2114/2114 [==============================] - 2s 776us/step - loss: 5.1238e-04 Epoch 379/500 2114/2114 [==============================] - 1s 637us/step - loss: 5.7623e-04 Epoch 380/500 2114/2114 [==============================] - 1s 609us/step - loss: 6.6705e-04 Epoch 381/500 2114/2114 [==============================] - 1s 669us/step - loss: 6.3447e-04 Epoch 382/500 2114/2114 [==============================] - 2s 721us/step - loss: 6.4624e-04 Epoch 383/500 2114/2114 [==============================] - 2s 765us/step - loss: 6.4104e-04 Epoch 384/500 2114/2114 [==============================] - 2s 737us/step - loss: 6.1133e-04 Epoch 385/500 2114/2114 [==============================] - 2s 850us/step - loss: 5.6873e-04 Epoch 386/500 2114/2114 [==============================] - 1s 704us/step - loss: 5.2896e-04 0s - loss: 4. Epoch 387/500 2114/2114 [==============================] - 2s 732us/step - loss: 5.4932e-04 Epoch 388/500 2114/2114 [==============================] - 2s 731us/step - loss: 5.8795e-04 Epoch 389/500 2114/2114 [==============================] - 2s 877us/step - loss: 5.8891e-04 Epoch 390/500 2114/2114 [==============================] - 1s 700us/step - loss: 5.8394e-04 Epoch 391/500 2114/2114 [==============================] - 1s 611us/step - loss: 5.5280e-04 0s - loss: 5.6317e- Epoch 392/500 2114/2114 [==============================] - 1s 600us/step - loss: 5.2377e-04 Epoch 393/500 2114/2114 [==============================] - 1s 630us/step - loss: 6.8849e-04 Epoch 394/500 2114/2114 [==============================] - 1s 693us/step - loss: 6.9413e-04 Epoch 395/500 2114/2114 [==============================] - 1s 698us/step - loss: 6.1279e-04 Epoch 396/500 2114/2114 [==============================] - 1s 699us/step - loss: 6.9257e-04 Epoch 397/500 2114/2114 [==============================] - 1s 684us/step - loss: 4.3880e-04 Epoch 398/500 2114/2114 [==============================] - 1s 705us/step - loss: 6.0916e-04 Epoch 399/500 2114/2114 [==============================] - 2s 757us/step - loss: 5.5144e-04 Epoch 400/500 2114/2114 [==============================] - 2s 729us/step - loss: 5.3151e-04 Epoch 401/500 2114/2114 [==============================] - 1s 704us/step - loss: 5.5134e-04 Epoch 402/500 2114/2114 [==============================] - 1s 618us/step - loss: 5.7441e-04 Epoch 403/500 2114/2114 [==============================] - 1s 686us/step - loss: 5.6589e-04 Epoch 404/500 2114/2114 [==============================] - 2s 712us/step - loss: 5.3812e-04 Epoch 405/500 2114/2114 [==============================] - 1s 638us/step - loss: 6.5880e-04 0s - loss: 6.7209e-0 - ETA: 0s - loss: 6 Epoch 406/500 2114/2114 [==============================] - 1s 700us/step - loss: 5.5460e-04 Epoch 407/500 2114/2114 [==============================] - 1s 637us/step - loss: 5.7486e-04 Epoch 408/500 2114/2114 [==============================] - 1s 621us/step - loss: 5.2520e-04 Epoch 409/500 2114/2114 [==============================] - 1s 650us/step - loss: 5.1756e-04 Epoch 410/500 2114/2114 [==============================] - 1s 651us/step - loss: 6.0069e-04 Epoch 411/500 2114/2114 [==============================] - 2s 828us/step - loss: 5.8862e-04 Epoch 412/500 2114/2114 [==============================] - 2s 787us/step - loss: 6.8862e-04 Epoch 413/500 2114/2114 [==============================] - 1s 604us/step - loss: 5.9450e-04 Epoch 414/500 2114/2114 [==============================] - 1s 600us/step - loss: 7.0046e-04 Epoch 415/500 2114/2114 [==============================] - 1s 633us/step - loss: 4.8695e-04 Epoch 416/500 2114/2114 [==============================] - 1s 652us/step - loss: 5.5362e-04 Epoch 417/500 2114/2114 [==============================] - 1s 617us/step - loss: 5.7044e-04 Epoch 418/500 2114/2114 [==============================] - 1s 607us/step - loss: 6.5182e-04 Epoch 419/500 2114/2114 [==============================] - 1s 632us/step - loss: 7.1265e-04 Epoch 420/500 2114/2114 [==============================] - 1s 612us/step - loss: 5.1906e-04 0s - loss: 6. Epoch 421/500 2114/2114 [==============================] - 1s 664us/step - loss: 5.1246e-04 Epoch 422/500 2114/2114 [==============================] - 2s 932us/step - loss: 6.3971e-04 Epoch 423/500 2114/2114 [==============================] - 1s 679us/step - loss: 5.5048e-04 Epoch 424/500 2114/2114 [==============================] - 1s 681us/step - loss: 5.3216e-04 Epoch 425/500 2114/2114 [==============================] - 1s 680us/step - loss: 5.5689e-04 Epoch 426/500 2114/2114 [==============================] - 1s 648us/step - loss: 5.7946e-04 0s - loss: 5.4 Epoch 427/500 2114/2114 [==============================] - 1s 645us/step - loss: 6.6615e-04 Epoch 428/500 2114/2114 [==============================] - 1s 652us/step - loss: 6.3112e-04 Epoch 429/500 2114/2114 [==============================] - 1s 685us/step - loss: 6.1640e-04 Epoch 430/500 2114/2114 [==============================] - 1s 691us/step - loss: 5.0742e-04 Epoch 431/500 2114/2114 [==============================] - 1s 668us/step - loss: 5.7810e-04 Epoch 432/500 2114/2114 [==============================] - 2s 711us/step - loss: 6.6876e-04 Epoch 433/500 2114/2114 [==============================] - 2s 893us/step - loss: 6.4580e-04 Epoch 434/500 2114/2114 [==============================] - 2s 728us/step - loss: 6.5976e-04 Epoch 435/500 2114/2114 [==============================] - 2s 741us/step - loss: 5.6575e-04 Epoch 436/500 2114/2114 [==============================] - 2s 734us/step - loss: 6.6602e-04 Epoch 437/500 2114/2114 [==============================] - 1s 707us/step - loss: 6.0253e-04 Epoch 438/500 2114/2114 [==============================] - 1s 696us/step - loss: 5.2597e-04 Epoch 439/500 2114/2114 [==============================] - 1s 687us/step - loss: 5.3381e-04 Epoch 440/500 2114/2114 [==============================] - 1s 683us/step - loss: 7.0305e-04 Epoch 441/500 2114/2114 [==============================] - 1s 655us/step - loss: 6.1364e-04 Epoch 442/500 2114/2114 [==============================] - 1s 634us/step - loss: 4.8041e-04 0s - l Epoch 443/500 2114/2114 [==============================] - 2s 788us/step - loss: 6.0458e-04 Epoch 444/500 2114/2114 [==============================] - 2s 832us/step - loss: 5.5862e-04 Epoch 445/500 2114/2114 [==============================] - 1s 688us/step - loss: 7.0646e-04 Epoch 446/500 2114/2114 [==============================] - 1s 629us/step - loss: 5.4308e-04 Epoch 447/500 2114/2114 [==============================] - 1s 641us/step - loss: 6.2576e-04 Epoch 448/500 2114/2114 [==============================] - 1s 648us/step - loss: 5.5083e-04 0s - loss: 5.50 Epoch 449/500 2114/2114 [==============================] - 1s 632us/step - loss: 5.8677e-04 Epoch 450/500 2114/2114 [==============================] - 1s 656us/step - loss: 5.9713e-04 Epoch 451/500 2114/2114 [==============================] - 1s 685us/step - loss: 5.4976e-04 Epoch 452/500 2114/2114 [==============================] - 2s 712us/step - loss: 4.9254e-04 Epoch 453/500 2114/2114 [==============================] - 1s 702us/step - loss: 5.4101e-04 Epoch 454/500 2114/2114 [==============================] - 2s 804us/step - loss: 4.9872e-04 Epoch 455/500 2114/2114 [==============================] - 2s 781us/step - loss: 4.8403e-04 Epoch 456/500 2114/2114 [==============================] - 2s 713us/step - loss: 4.9325e-04 Epoch 457/500 2114/2114 [==============================] - 1s 662us/step - loss: 5.7780e-04 Epoch 458/500 2114/2114 [==============================] - 1s 664us/step - loss: 5.3627e-04 Epoch 459/500 2114/2114 [==============================] - 1s 670us/step - loss: 5.2742e-04 Epoch 460/500 2114/2114 [==============================] - 1s 636us/step - loss: 5.0896e-04 Epoch 461/500 2114/2114 [==============================] - 1s 621us/step - loss: 6.7138e-04 Epoch 462/500 2114/2114 [==============================] - 1s 683us/step - loss: 5.1272e-04 Epoch 463/500 2114/2114 [==============================] - 1s 657us/step - loss: 5.9007e-04 Epoch 464/500 2114/2114 [==============================] - 1s 622us/step - loss: 5.4225e-04 Epoch 465/500 2114/2114 [==============================] - 1s 635us/step - loss: 6.0678e-04 0s - loss: 6.3638 Epoch 466/500 2114/2114 [==============================] - 2s 815us/step - loss: 6.1269e-04 Epoch 467/500 2114/2114 [==============================] - 1s 698us/step - loss: 5.9724e-04 Epoch 468/500 2114/2114 [==============================] - 2s 729us/step - loss: 6.1807e-04 Epoch 469/500 2114/2114 [==============================] - 2s 725us/step - loss: 5.2630e-04 Epoch 470/500 2114/2114 [==============================] - 2s 755us/step - loss: 6.6079e-04 Epoch 471/500 2114/2114 [==============================] - 2s 758us/step - loss: 6.2403e-04 Epoch 472/500 2114/2114 [==============================] - 1s 700us/step - loss: 5.5914e-04 Epoch 473/500 2114/2114 [==============================] - 1s 692us/step - loss: 6.8727e-04 Epoch 474/500 2114/2114 [==============================] - 1s 678us/step - loss: 5.2621e-04 Epoch 475/500 2114/2114 [==============================] - 1s 679us/step - loss: 5.9292e-04 Epoch 476/500 2114/2114 [==============================] - 2s 905us/step - loss: 4.9175e-04 Epoch 477/500 2114/2114 [==============================] - 2s 717us/step - loss: 6.1136e-04 Epoch 478/500 2114/2114 [==============================] - 1s 699us/step - loss: 5.3795e-04 Epoch 479/500 2114/2114 [==============================] - 1s 652us/step - loss: 5.3394e-04 0s - loss: 5.0745e Epoch 480/500 2114/2114 [==============================] - 1s 665us/step - loss: 7.2797e-04 Epoch 481/500 2114/2114 [==============================] - 1s 694us/step - loss: 5.5138e-04 Epoch 482/500 2114/2114 [==============================] - 1s 689us/step - loss: 5.2983e-04 Epoch 483/500 2114/2114 [==============================] - 1s 652us/step - loss: 5.2063e-04 1s Epoch 484/500 2114/2114 [==============================] - 1s 706us/step - loss: 6.2069e-04 Epoch 485/500 2114/2114 [==============================] - 2s 747us/step - loss: 5.0298e-04 Epoch 486/500 2114/2114 [==============================] - 2s 741us/step - loss: 5.2104e-04 Epoch 487/500 2114/2114 [==============================] - 2s 1ms/step - loss: 7.0094e-04 Epoch 488/500 2114/2114 [==============================] - 2s 763us/step - loss: 6.4404e-04 Epoch 489/500 2114/2114 [==============================] - 2s 901us/step - loss: 6.5419e-04 Epoch 490/500 2114/2114 [==============================] - 2s 763us/step - loss: 5.4483e-04 Epoch 491/500 2114/2114 [==============================] - 1s 707us/step - loss: 5.7262e-04 Epoch 492/500 2114/2114 [==============================] - 2s 745us/step - loss: 5.5717e-04 Epoch 493/500 2114/2114 [==============================] - 2s 908us/step - loss: 6.5252e-04 Epoch 494/500 2114/2114 [==============================] - 1s 670us/step - loss: 6.2740e-04 Epoch 495/500 2114/2114 [==============================] - 2s 882us/step - loss: 6.9801e-04 Epoch 496/500 2114/2114 [==============================] - 2s 977us/step - loss: 5.7754e-04 Epoch 497/500 2114/2114 [==============================] - 2s 749us/step - loss: 5.7544e-04 Epoch 498/500 2114/2114 [==============================] - 1s 681us/step - loss: 5.8934e-04 Epoch 499/500 2114/2114 [==============================] - 2s 711us/step - loss: 5.5475e-04 Epoch 500/500 2114/2114 [==============================] - 2s 713us/step - loss: 5.9717e-04
Loss function for RELU¶
In [38]:
pyplot.title('Mean Squared Error')
pyplot.plot(historyTwo.history['loss'], label='train')
pyplot.legend()
pyplot.show()
In [39]:
all_priceRulu = sigRulu.predict(X_train_3D)
In [40]:
All_inverseRulu_T =np.concatenate((X_train_4Nor, all_priceRulu), axis=1)
In [41]:
All_inverseRulu_T = pd.DataFrame(All_inverseRulu_T,columns=['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15'])
All_inverseRulu_T.tail(5)
Out[41]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2109 | 0.008459 | 0.000521 | 3.606515e-06 | 0.489011 | 0.014703 | 0.0 | 0.101531 | 0.742215 | 0.000944 | 0.000000 | 0.022339 | 0.007341 | 0.000060 | 0.012552 | 0.000348 | 0.005671 |
| 2110 | 0.009717 | 0.000594 | 1.832925e-06 | 0.472527 | 0.027416 | 0.0 | 0.193424 | 0.726644 | 0.000992 | 0.006305 | 0.027228 | 0.008202 | 0.000047 | 0.014367 | 0.000402 | 0.006868 |
| 2111 | 0.004895 | 0.000302 | 8.352798e-07 | 0.357143 | 0.010709 | 0.0 | 0.110597 | 0.617647 | 0.000774 | 0.100000 | 0.026916 | 0.002917 | 0.000033 | 0.007157 | 0.000318 | 0.008878 |
| 2112 | 0.004622 | 0.000381 | 8.352798e-07 | 0.452381 | 0.010099 | 0.0 | 0.109903 | 0.707612 | 0.000745 | 0.100000 | 0.024805 | 0.003172 | 0.000010 | 0.007832 | 0.000309 | 0.009508 |
| 2113 | 0.006650 | 0.000570 | 0.000000e+00 | 0.410256 | 0.016235 | 0.0 | 0.126421 | 0.667820 | 0.001006 | 0.000000 | 0.026426 | 0.003908 | 0.000000 | 0.009226 | 0.000361 | 0.005477 |
In [42]:
columnsTitles = ['0','1','2','3','4','5','6','7','8','9','10','11','15','13','14','12']
All_inverseRulu_T=All_inverseRulu_T.reindex(columns=columnsTitles)
All_inverseRulu_T =All_inverseRulu_T[::-1]
All_inverseRulu_T.head(5)
Out[42]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 15 | 13 | 14 | 12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2113 | 0.006650 | 0.000570 | 0.000000e+00 | 0.410256 | 0.016235 | 0.0 | 0.126421 | 0.667820 | 0.001006 | 0.000000 | 0.026426 | 0.003908 | 0.005477 | 0.009226 | 0.000361 | 0.000000 |
| 2112 | 0.004622 | 0.000381 | 8.352798e-07 | 0.452381 | 0.010099 | 0.0 | 0.109903 | 0.707612 | 0.000745 | 0.100000 | 0.024805 | 0.003172 | 0.009508 | 0.007832 | 0.000309 | 0.000010 |
| 2111 | 0.004895 | 0.000302 | 8.352798e-07 | 0.357143 | 0.010709 | 0.0 | 0.110597 | 0.617647 | 0.000774 | 0.100000 | 0.026916 | 0.002917 | 0.008878 | 0.007157 | 0.000318 | 0.000033 |
| 2110 | 0.009717 | 0.000594 | 1.832925e-06 | 0.472527 | 0.027416 | 0.0 | 0.193424 | 0.726644 | 0.000992 | 0.006305 | 0.027228 | 0.008202 | 0.006868 | 0.014367 | 0.000402 | 0.000047 |
| 2109 | 0.008459 | 0.000521 | 3.606515e-06 | 0.489011 | 0.014703 | 0.0 | 0.101531 | 0.742215 | 0.000944 | 0.000000 | 0.022339 | 0.007341 | 0.005671 | 0.012552 | 0.000348 | 0.000060 |
In [43]:
All_inverseRulu_T = np.array(All_inverseRulu_T)
In [44]:
All_inverseRulu_T = trainMinMax.inverse_transform(All_inverseRulu_T)
In [45]:
All_inverseRulu_T
Out[45]:
array([[1.10500000e+04, 1.82345776e+06, 7.98626858e+02, ...,
1.01005364e+08, 2.23377243e+03, 4.48450527e+05],
[9.83200000e+03, 1.34492997e+06, 8.08548894e+02, ...,
9.08518825e+07, 2.22220199e+03, 5.71106414e+05],
[9.99600000e+03, 1.14548939e+06, 8.08548894e+02, ...,
8.59378578e+07, 2.22424209e+03, 8.41514530e+05],
...,
[7.06690000e+04, 8.83410322e+07, 1.18794958e+07, ...,
7.44612429e+08, 5.74445943e+03, 7.39894557e+09],
[9.98370000e+04, 9.13641667e+07, 1.18794958e+07, ...,
7.80214701e+08, 5.94215548e+03, 7.40803421e+09],
[1.02834000e+05, 2.72754401e+08, 1.18150952e+07, ...,
8.26556232e+08, 1.17095829e+04, 7.42680941e+09]])
In [46]:
All_inverseRulu_T=All_inverseRulu_T[:,12]
All_inverseRulu_T = All_inverseRulu_T[::-1]
Two layer with Two sigmoid fuction.¶
In [47]:
regressor = Sequential()
#Adding the input layer and the LSTM layer
regressor.add(LSTM(units = 50, activation = 'sigmoid',return_sequences=True, input_shape = (None, 1)))
#regressor.add(LSTM(units=20,return_sequences=True))
#Adding the output layer
regressor.add(LSTM(units = 100, return_sequences=False))
regressor.add(Dropout(rate=.2))
regressor.add(Dense(units = 1))
#Compiling the Recurrent Neural Network
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
Batch size 50 and epoch 500¶
In [48]:
#Fitting the Recurrent Neural Network [epoches is a kindoff number of iteration]
history= regressor.fit(X_train_3D, y_train, batch_size = 50, epochs = 500)
Epoch 1/500 2114/2114 [==============================] - 5s 2ms/step - loss: 0.0332 Epoch 2/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0240 Epoch 3/500 2114/2114 [==============================] - 5s 2ms/step - loss: 0.0233 Epoch 4/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0210 Epoch 5/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0188 Epoch 6/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0139 Epoch 7/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0125 Epoch 8/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0059 Epoch 9/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0047 Epoch 10/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0044 Epoch 11/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0038 Epoch 12/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0046 Epoch 13/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0035 Epoch 14/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0043 Epoch 15/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0039 Epoch 16/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0030 Epoch 17/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0032 Epoch 18/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0034 Epoch 19/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0034 Epoch 20/500 2114/2114 [==============================] - 2s 1ms/step - loss: 0.0034 Epoch 21/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0032 Epoch 22/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0031 Epoch 23/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0030 Epoch 24/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0028 Epoch 25/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0029 Epoch 26/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0028 Epoch 27/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0028 Epoch 28/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0030 Epoch 29/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0027 Epoch 30/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0027A: 0s - loss: Epoch 31/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0026 Epoch 32/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0021 Epoch 33/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0020A: 3s - loss: Epoch 34/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0019A: 0s - Epoch 35/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0024A: 0s - loss: 0. Epoch 36/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0021 Epoch 37/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0019 Epoch 38/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0023 Epoch 39/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0018 Epoch 40/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0018 Epoch 41/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0020 Epoch 42/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0019 Epoch 43/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0016A: 0s - lo Epoch 44/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0016 Epoch 45/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0019 Epoch 46/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0016 Epoch 47/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0017 Epoch 48/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0027 Epoch 49/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0016 Epoch 50/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0014 Epoch 51/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0015 Epoch 52/500 2114/2114 [==============================] - 15s 7ms/step - loss: 0.0016 Epoch 53/500 2114/2114 [==============================] - 9s 4ms/step - loss: 0.0013 Epoch 54/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0016 Epoch 55/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0015A Epoch 56/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0016 Epoch 57/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0015 Epoch 58/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0016 Epoch 59/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0013 Epoch 60/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0011 Epoch 61/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0011 Epoch 62/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0013 Epoch 63/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0011 Epoch 64/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0016 Epoch 65/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0012 Epoch 66/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0011 Epoch 67/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0011 Epoch 68/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0013 Epoch 69/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0015 Epoch 70/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0013 Epoch 71/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0012 Epoch 72/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.8073e-04 Epoch 73/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.8144e-04 Epoch 74/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0010TA: 1s - Epoch 75/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.7115e-04 Epoch 76/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0012 Epoch 77/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.9893e-04 Epoch 78/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0010 Epoch 79/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.7196e-04A: Epoch 80/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0011 Epoch 81/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.0602e-04 Epoch 82/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.8873e-04 Epoch 83/500 2114/2114 [==============================] - 3s 1ms/step - loss: 0.0014 Epoch 84/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.7659e-04 Epoch 85/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0014A: 0s - loss: 0.001 Epoch 86/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0011 Epoch 87/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.4204e-04 Epoch 88/500 2114/2114 [==============================] - 3s 2ms/step - loss: 8.8479e-04 Epoch 89/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.0993e-04 Epoch 90/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.1352e-04 Epoch 91/500 2114/2114 [==============================] - 4s 2ms/step - loss: 0.0010 Epoch 92/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.0273e-04A: 0s - loss: 9. Epoch 93/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.0127e-04 Epoch 94/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.1302e-04 Epoch 95/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.8116e-04 Epoch 96/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.0560e-04 Epoch 97/500 2114/2114 [==============================] - ETA: 0s - loss: 6.4673e-0 - 3s 2ms/step - loss: 6.4582e-04 Epoch 98/500 2114/2114 [==============================] - 4s 2ms/step - loss: 7.3751e-04 Epoch 99/500 2114/2114 [==============================] - 3s 1ms/step - loss: 8.0740e-04 Epoch 100/500 2114/2114 [==============================] - 3s 1ms/step - loss: 7.2674e-04 Epoch 101/500 2114/2114 [==============================] - 3s 2ms/step - loss: 7.7812e-04 Epoch 102/500 2114/2114 [==============================] - 4s 2ms/step - loss: 9.3670e-04 Epoch 103/500 2114/2114 [==============================] - 3s 2ms/step - loss: 0.0010 Epoch 104/500 2114/2114 [==============================] - 3s 2ms/step - loss: 8.6903e-04 Epoch 105/500 2114/2114 [==============================] - 4s 2ms/step - loss: 8.2241e-04 Epoch 106/500 2114/2114 [==============================] - 3s 2ms/step - loss: 6.3994e-04 Epoch 107/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.6839e-04 Epoch 108/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.6768e-04 Epoch 109/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.0203e-04 Epoch 110/500 2114/2114 [==============================] - 3s 1ms/step - loss: 6.6614e-04 Epoch 111/500 2114/2114 [==============================] - 3s 2ms/step - loss: 6.2047e-04 - ETA: 1s - los Epoch 112/500 2114/2114 [==============================] - 4s 2ms/step - loss: 5.2014e-04A: 1s - Epoch 113/500 2114/2114 [==============================] - 4s 2ms/step - loss: 4.7496e-04 Epoch 114/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.9196e-04 Epoch 115/500 2114/2114 [==============================] - 3s 2ms/step - loss: 6.4194e-04A: 1s - loss: Epoch 116/500 2114/2114 [==============================] - 4s 2ms/step - loss: 5.1877e-04 Epoch 117/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.6777e-04 Epoch 118/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.5813e-04 Epoch 119/500 2114/2114 [==============================] - 3s 2ms/step - loss: 4.3185e-04 Epoch 120/500 2114/2114 [==============================] - 3s 2ms/step - loss: 5.0102e-04 Epoch 121/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.9666e-04 Epoch 122/500 2114/2114 [==============================] - 3s 2ms/step - loss: 4.4981e-04 Epoch 123/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.8278e-04 Epoch 124/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.8965e-04 Epoch 125/500 2114/2114 [==============================] - 3s 2ms/step - loss: 5.2412e-04 Epoch 126/500 2114/2114 [==============================] - 4s 2ms/step - loss: 6.8184e-04 Epoch 127/500 2114/2114 [==============================] - 4s 2ms/step - loss: 4.5531e-04 Epoch 128/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.2232e-04A: 0s - loss: 3.2120e Epoch 129/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.5320e-04 Epoch 130/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.2330e-04 Epoch 131/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.0737e-04 Epoch 132/500 2114/2114 [==============================] - 3s 1ms/step - loss: 4.5491e-04A: 1s - l Epoch 133/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.9946e-04 Epoch 134/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.6395e-04 Epoch 135/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.6918e-04 Epoch 136/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.3348e-04 Epoch 137/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.0086e-04 Epoch 138/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.3588e-04 Epoch 139/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.6628e-04 Epoch 140/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.8703e-04 Epoch 141/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.5010e-04 Epoch 142/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.7672e-04 Epoch 143/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.7065e-04 Epoch 144/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.8583e-04 Epoch 145/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.4091e-04 Epoch 146/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.1756e-04 Epoch 147/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.9079e-04 Epoch 148/500 2114/2114 [==============================] - 3s 2ms/step - loss: 5.7373e-04 Epoch 149/500 2114/2114 [==============================] - 4s 2ms/step - loss: 5.7969e-04A: 0s - loss: 6 Epoch 150/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.8725e-04 Epoch 151/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.9320e-04 Epoch 152/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.8690e-04 Epoch 153/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.5104e-04A: 1s Epoch 154/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.3038e-04 Epoch 155/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.2158e-04 Epoch 156/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.6565e-04 Epoch 157/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.9428e-04 Epoch 158/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.9925e-04 Epoch 159/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.4497e-04 Epoch 160/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.9786e-04 Epoch 161/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.2130e-04 Epoch 162/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0761e-04 Epoch 163/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.6918e-04A: Epoch 164/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.8629e-04 Epoch 165/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.9665e-04 Epoch 166/500 2114/2114 [==============================] - 3s 2ms/step - loss: 4.1024e-04 Epoch 167/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.8297e-04 Epoch 168/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0585e-04 Epoch 169/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.9193e-04 Epoch 170/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.9254e-04 Epoch 171/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.7587e-04 Epoch 172/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.4907e-04 Epoch 173/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.1643e-04 Epoch 174/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.8697e-04A: 1s - lo Epoch 175/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.2233e-04 Epoch 176/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.6807e-04 Epoch 177/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.7965e-04 Epoch 178/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.1536e-04 Epoch 179/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.9148e-04 Epoch 180/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.3736e-04 Epoch 181/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.6998e-04 Epoch 182/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0852e-04 Epoch 183/500 2114/2114 [==============================] - 4s 2ms/step - loss: 5.0086e-04 Epoch 184/500 2114/2114 [==============================] - 3s 1ms/step - loss: 4.6225e-04 Epoch 185/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.7628e-04 Epoch 186/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.0614e-04 Epoch 187/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.1166e-04 Epoch 188/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.9110e-04 Epoch 189/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.4782e-04 Epoch 190/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.3357e-04 Epoch 191/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.1801e-04 Epoch 192/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.2420e-04 Epoch 193/500 2114/2114 [==============================] - 7s 3ms/step - loss: 2.7116e-04 Epoch 194/500 2114/2114 [==============================] - 7s 3ms/step - loss: 2.8263e-04 Epoch 195/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0502e-04 Epoch 196/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.5821e-04 Epoch 197/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.5806e-04 Epoch 198/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.4687e-04 Epoch 199/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.2211e-04 Epoch 200/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.4345e-04 Epoch 201/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.0571e-04 Epoch 202/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8527e-04 Epoch 203/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.1930e-04 Epoch 204/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.1542e-04 Epoch 205/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.6399e-04 Epoch 206/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.2363e-04 Epoch 207/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.9999e-04 Epoch 208/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.3534e-04 Epoch 209/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.1945e-04 Epoch 210/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.2033e-04 Epoch 211/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.5831e-04 Epoch 212/500 2114/2114 [==============================] - 5s 3ms/step - loss: 3.0624e-04 Epoch 213/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.0979e-04 Epoch 214/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.8843e-04 Epoch 215/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.1653e-04 Epoch 216/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.0565e-04 Epoch 217/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.7755e-04 Epoch 218/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.1905e-04 Epoch 219/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.1880e-04 Epoch 220/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.6817e-04 Epoch 221/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.8623e-04 Epoch 222/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.8234e-04 Epoch 223/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.7086e-04 Epoch 224/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.5529e-04A: 3s - loss: 2.6 Epoch 225/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.6380e-04 Epoch 226/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.7268e-04 Epoch 227/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.8432e-04 Epoch 228/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.2966e-04 Epoch 229/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.8541e-04 Epoch 230/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.9515e-04 Epoch 231/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0227e-04 Epoch 232/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.1986e-04 Epoch 233/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.9705e-04 Epoch 234/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.3719e-04 Epoch 235/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.0219e-04 Epoch 236/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.7045e-04A: 4s - loss: Epoch 237/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.8997e-04 Epoch 238/500 2114/2114 [==============================] - 6s 3ms/step - loss: 3.4467e-04 Epoch 239/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.6838e-04 Epoch 240/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.0472e-04 Epoch 241/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.6158e-04 Epoch 242/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.4511e-04 Epoch 243/500 2114/2114 [==============================] - 6s 3ms/step - loss: 3.2204e-04 Epoch 244/500 2114/2114 [==============================] - 5s 3ms/step - loss: 2.7988e-04 Epoch 245/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.8549e-04 Epoch 246/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.9265e-04 Epoch 247/500 2114/2114 [==============================] - 6s 3ms/step - loss: 2.8529e-04 Epoch 248/500 2114/2114 [==============================] - 6s 3ms/step - loss: 3.0551e-04 Epoch 249/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.3821e-04 Epoch 250/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.9251e-04 Epoch 251/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.9480e-04A: 0s - loss: 3 Epoch 252/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.7784e-04 Epoch 253/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.8314e-04 Epoch 254/500 2114/2114 [==============================] - 3s 2ms/step - loss: 4.0069e-04 Epoch 255/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.2687e-04 Epoch 256/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.2142e-04 Epoch 257/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.9411e-04 Epoch 258/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0049e-04 Epoch 259/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.8948e-04 Epoch 260/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.7479e-04A: 0s - loss Epoch 261/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.7872e-04 Epoch 262/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.5647e-04 Epoch 263/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.3781e-04 Epoch 264/500 2114/2114 [==============================] - 3s 2ms/step - loss: 3.2173e-04 Epoch 265/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.8702e-04 Epoch 266/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.8970e-04 Epoch 267/500 2114/2114 [==============================] - 5s 3ms/step - loss: 2.8849e-04 Epoch 268/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.8620e-04 Epoch 269/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.3141e-04 Epoch 270/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.4660e-04 Epoch 271/500 2114/2114 [==============================] - 6s 3ms/step - loss: 2.9489e-04 Epoch 272/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.7152e-04 Epoch 273/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.1383e-04 Epoch 274/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.4515e-04 Epoch 275/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.5165e-04 Epoch 276/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.5798e-04 Epoch 277/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.3588e-04 Epoch 278/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.4743e-04 Epoch 279/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.9206e-04 Epoch 280/500 2114/2114 [==============================] - 4s 2ms/step - loss: 3.0801e-04 Epoch 281/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.1022e-04 Epoch 282/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8033e-04 Epoch 283/500 2114/2114 [==============================] - 3s 1ms/step - loss: 3.8378e-04 Epoch 284/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.6908e-04 Epoch 285/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8205e-04 Epoch 286/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.6425e-04A: 0s - loss: 2.590 Epoch 287/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8372e-04 Epoch 288/500 2114/2114 [==============================] - 2s 932us/step - loss: 2.6256e-04 Epoch 289/500 2114/2114 [==============================] - 2s 998us/step - loss: 2.5096e-04 Epoch 290/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.5154e-04 Epoch 291/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8118e-04 Epoch 292/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.5928e-04 Epoch 293/500 2114/2114 [==============================] - 2s 978us/step - loss: 2.5442e-04 Epoch 294/500 2114/2114 [==============================] - 2s 779us/step - loss: 2.2614e-04 Epoch 295/500 2114/2114 [==============================] - 2s 981us/step - loss: 2.2088e-04 Epoch 296/500 2114/2114 [==============================] - 2s 988us/step - loss: 2.1938e-04 0s - loss: 2.23 Epoch 297/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.6017e-04 Epoch 298/500 2114/2114 [==============================] - 2s 950us/step - loss: 2.3487e-04 Epoch 299/500 2114/2114 [==============================] - 2s 919us/step - loss: 2.4104e-04 Epoch 300/500 2114/2114 [==============================] - 2s 860us/step - loss: 2.3943e-04 2s Epoch 301/500 2114/2114 [==============================] - 2s 929us/step - loss: 3.1226e-04 Epoch 302/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8960e-04 Epoch 303/500 2114/2114 [==============================] - 2s 796us/step - loss: 2.8266e-04 Epoch 304/500 2114/2114 [==============================] - 2s 980us/step - loss: 2.4914e-04 Epoch 305/500 2114/2114 [==============================] - 2s 857us/step - loss: 2.6700e-04 Epoch 306/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.9557e-04 Epoch 307/500 2114/2114 [==============================] - 2s 961us/step - loss: 2.2928e-04 Epoch 308/500 2114/2114 [==============================] - 2s 979us/step - loss: 2.7006e-04 Epoch 309/500 2114/2114 [==============================] - 2s 956us/step - loss: 2.2840e-04 Epoch 310/500 2114/2114 [==============================] - 2s 925us/step - loss: 2.0634e-04 Epoch 311/500 2114/2114 [==============================] - 2s 930us/step - loss: 2.7416e-04 Epoch 312/500 2114/2114 [==============================] - 2s 962us/step - loss: 2.6714e-04 Epoch 313/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.7127e-04 Epoch 314/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.6142e-04 Epoch 315/500 2114/2114 [==============================] - 2s 927us/step - loss: 2.1871e-04 Epoch 316/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.6571e-04 Epoch 317/500 2114/2114 [==============================] - 2s 930us/step - loss: 2.5915e-04 Epoch 318/500 2114/2114 [==============================] - 2s 963us/step - loss: 2.4707e-04 Epoch 319/500 2114/2114 [==============================] - 2s 972us/step - loss: 2.7873e-04 Epoch 320/500 2114/2114 [==============================] - 2s 928us/step - loss: 2.8909e-04 Epoch 321/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.2968e-04 Epoch 322/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.2197e-04 Epoch 323/500 2114/2114 [==============================] - 2s 827us/step - loss: 3.5325e-04 Epoch 324/500 2114/2114 [==============================] - 2s 864us/step - loss: 2.3837e-04 Epoch 325/500 2114/2114 [==============================] - 2s 923us/step - loss: 2.6844e-04 0s - loss: 2.740 Epoch 326/500 2114/2114 [==============================] - 2s 988us/step - loss: 2.5467e-04 Epoch 327/500 2114/2114 [==============================] - 2s 835us/step - loss: 2.9485e-04 Epoch 328/500 2114/2114 [==============================] - 2s 913us/step - loss: 2.5123e-04 Epoch 329/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.0608e-04 Epoch 330/500 2114/2114 [==============================] - 2s 898us/step - loss: 2.4486e-04 Epoch 331/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3115e-04 Epoch 332/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.7032e-04 Epoch 333/500 2114/2114 [==============================] - 2s 850us/step - loss: 2.2934e-04 Epoch 334/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8474e-04 Epoch 335/500 2114/2114 [==============================] - 2s 853us/step - loss: 2.1039e-04 Epoch 336/500 2114/2114 [==============================] - 2s 844us/step - loss: 2.5646e-04 Epoch 337/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3658e-04 Epoch 338/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8148e-04 Epoch 339/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.2052e-04 Epoch 340/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3349e-04 Epoch 341/500 2114/2114 [==============================] - 2s 832us/step - loss: 2.5869e-04 Epoch 342/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.0411e-04 Epoch 343/500 2114/2114 [==============================] - 2s 858us/step - loss: 2.9290e-04 Epoch 344/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.3354e-04 Epoch 345/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.7532e-04 Epoch 346/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.8376e-04 Epoch 347/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.1489e-04 Epoch 348/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3604e-04 Epoch 349/500 2114/2114 [==============================] - 2s 974us/step - loss: 2.5201e-04 Epoch 350/500 2114/2114 [==============================] - 2s 935us/step - loss: 3.3550e-04 Epoch 351/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3469e-04 Epoch 352/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.1604e-04 Epoch 353/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3515e-04 Epoch 354/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.2365e-04 Epoch 355/500 2114/2114 [==============================] - 2s 917us/step - loss: 2.2164e-04 Epoch 356/500 2114/2114 [==============================] - 2s 907us/step - loss: 2.3993e-04 Epoch 357/500 2114/2114 [==============================] - 2s 889us/step - loss: 2.1539e-04 Epoch 358/500 2114/2114 [==============================] - 2s 980us/step - loss: 2.3488e-04 Epoch 359/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.5768e-04 Epoch 360/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.5867e-04 Epoch 361/500 2114/2114 [==============================] - 2s 975us/step - loss: 2.0135e-04 Epoch 362/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.1045e-04 Epoch 363/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.5419e-04 Epoch 364/500 2114/2114 [==============================] - 2s 950us/step - loss: 2.2892e-04 Epoch 365/500 2114/2114 [==============================] - 2s 884us/step - loss: 2.5948e-04 Epoch 366/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.0719e-04 Epoch 367/500 2114/2114 [==============================] - 2s 966us/step - loss: 2.0663e-04 Epoch 368/500 2114/2114 [==============================] - 2s 1ms/step - loss: 3.5254e-04 Epoch 369/500 2114/2114 [==============================] - 2s 923us/step - loss: 2.4706e-04 Epoch 370/500 2114/2114 [==============================] - 2s 938us/step - loss: 2.6146e-04 Epoch 371/500 2114/2114 [==============================] - 2s 969us/step - loss: 2.2943e-04 Epoch 372/500 2114/2114 [==============================] - 2s 982us/step - loss: 2.3185e-04 Epoch 373/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.0513e-04 Epoch 374/500 2114/2114 [==============================] - 2s 1ms/step - loss: 1.9042e-04 Epoch 375/500 2114/2114 [==============================] - 2s 961us/step - loss: 1.7345e-04 Epoch 376/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.0329e-04 Epoch 377/500 2114/2114 [==============================] - 2s 977us/step - loss: 2.1797e-04 Epoch 378/500 2114/2114 [==============================] - 2s 873us/step - loss: 2.1467e-04 Epoch 379/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.5914e-04 Epoch 380/500 2114/2114 [==============================] - 2s 860us/step - loss: 2.3387e-04 Epoch 381/500 2114/2114 [==============================] - 2s 1ms/step - loss: 4.3513e-04 Epoch 382/500 2114/2114 [==============================] - 2s 981us/step - loss: 2.0482e-04 Epoch 383/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.6611e-04 Epoch 384/500 2114/2114 [==============================] - 2s 1ms/step - loss: 2.3282e-04 Epoch 385/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.3784e-04 Epoch 386/500 2114/2114 [==============================] - 6s 3ms/step - loss: 1.9638e-04 Epoch 387/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.0167e-04 Epoch 388/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.3254e-04 Epoch 389/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.1463e-04A: Epoch 390/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.6562e-04 Epoch 391/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.0319e-04 Epoch 392/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.1231e-04 Epoch 393/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.3794e-04 Epoch 394/500 2114/2114 [==============================] - 5s 2ms/step - loss: 1.8611e-04 Epoch 395/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.6281e-04 Epoch 396/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.7721e-04 Epoch 397/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.8664e-04 Epoch 398/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.2489e-04 Epoch 399/500 2114/2114 [==============================] - 5s 2ms/step - loss: 3.0105e-04 Epoch 400/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.0035e-04 Epoch 401/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.1873e-04 Epoch 402/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.3437e-04 Epoch 403/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.9085e-04 Epoch 404/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.8782e-04 Epoch 405/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.3997e-04 Epoch 406/500 2114/2114 [==============================] - 5s 2ms/step - loss: 1.6991e-04 Epoch 407/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.8884e-04 Epoch 408/500 2114/2114 [==============================] - 5s 2ms/step - loss: 2.0921e-04 Epoch 409/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.9006e-04 Epoch 410/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.7638e-04 Epoch 411/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.0824e-04 Epoch 412/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.3161e-04 Epoch 413/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.4910e-04 Epoch 414/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.4452e-04 Epoch 415/500 2114/2114 [==============================] - ETA: 0s - loss: 2.1235e-0 - 3s 1ms/step - loss: 2.1165e-04 Epoch 416/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.4350e-04 Epoch 417/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7802e-04 Epoch 418/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.9123e-04 Epoch 419/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.4742e-04 Epoch 420/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.3027e-04 Epoch 421/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.2316e-04 Epoch 422/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7672e-04 Epoch 423/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.8839e-04 Epoch 424/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6283e-04 Epoch 425/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7652e-04 Epoch 426/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.5076e-04 Epoch 427/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6413e-04 Epoch 428/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.9054e-04 Epoch 429/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6916e-04 Epoch 430/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6085e-04 Epoch 431/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.0020e-04 Epoch 432/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.8471e-04 Epoch 433/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.5685e-04 Epoch 434/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.8666e-04 Epoch 435/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7218e-04 Epoch 436/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.7647e-04 Epoch 437/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.6141e-04 Epoch 438/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.6791e-04 Epoch 439/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.5391e-04 Epoch 440/500 2114/2114 [==============================] - 3s 2ms/step - loss: 2.2313e-04 Epoch 441/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.8015e-04 Epoch 442/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.0229e-04 Epoch 443/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.5514e-04 Epoch 444/500 2114/2114 [==============================] - 6s 3ms/step - loss: 1.9525e-04 Epoch 445/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.9675e-04 Epoch 446/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.8331e-04 Epoch 447/500 2114/2114 [==============================] - 6s 3ms/step - loss: 3.1877e-04A: 2 Epoch 448/500 2114/2114 [==============================] - 6s 3ms/step - loss: 2.0813e-04 Epoch 449/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.0973e-04 Epoch 450/500 2114/2114 [==============================] - 4s 2ms/step - loss: 2.0178e-04 Epoch 451/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.7174e-04 Epoch 452/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.7242e-04 Epoch 453/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.5808e-04 Epoch 454/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.1441e-04 Epoch 455/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.4371e-04 Epoch 456/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.0417e-04 Epoch 457/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.9976e-04 Epoch 458/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.0031e-04 Epoch 459/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7223e-04 Epoch 460/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.4881e-04 Epoch 461/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.3665e-04 Epoch 462/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.4583e-04 Epoch 463/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.3881e-04 Epoch 464/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.4664e-04 Epoch 465/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.3120e-04 Epoch 466/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.7737e-04 Epoch 467/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7387e-04 Epoch 468/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.9231e-04 Epoch 469/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.5010e-04 Epoch 470/500 2114/2114 [==============================] - 4s 2ms/step - loss: 1.8187e-04 Epoch 471/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6060e-04 Epoch 472/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7631e-04 Epoch 473/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.8053e-04A: 1 Epoch 474/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6587e-04A: 0s - loss: 1.6444e Epoch 475/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6112e-04 Epoch 476/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7722e-04 Epoch 477/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6619e-04 Epoch 478/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.1198e-04 Epoch 479/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7401e-04 Epoch 480/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.3530e-04 Epoch 481/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.7237e-04 Epoch 482/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6373e-04 Epoch 483/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.5776e-04 Epoch 484/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.3569e-04 Epoch 485/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6030e-04 Epoch 486/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.6609e-04 Epoch 487/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.3351e-04 Epoch 488/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.4054e-04 Epoch 489/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6504e-04 Epoch 490/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6629e-04 Epoch 491/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.5744e-04 Epoch 492/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.0976e-04 Epoch 493/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.2540e-04 Epoch 494/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.5548e-04 Epoch 495/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.6888e-04 Epoch 496/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.7195e-04 Epoch 497/500 2114/2114 [==============================] - 3s 2ms/step - loss: 1.5940e-04 Epoch 498/500 2114/2114 [==============================] - 3s 1ms/step - loss: 2.4357e-04 Epoch 499/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.8632e-04 Epoch 500/500 2114/2114 [==============================] - 3s 1ms/step - loss: 1.2597e-04
In [49]:
regressor.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm_3 (LSTM) (None, None, 50) 10400 _________________________________________________________________ lstm_4 (LSTM) (None, 100) 60400 _________________________________________________________________ dropout_2 (Dropout) (None, 100) 0 _________________________________________________________________ dense_2 (Dense) (None, 1) 101 ================================================================= Total params: 70,901 Trainable params: 70,901 Non-trainable params: 0 _________________________________________________________________
Loss function for two sigmod¶
In [50]:
pyplot.title('Mean Squared Error')
pyplot.plot(history.history['loss'], label='train')
pyplot.legend()
pyplot.show()
In [51]:
all_price = regressor.predict(X_train_3D)
In [52]:
#nomal X_train + pred
All_inverse_T =np.concatenate((X_train_4Nor, all_price), axis=1)
Denormalization¶
In [53]:
All_inverse_T = pd.DataFrame(All_inverse_T,columns=['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15'])
All_inverse_T.tail(5)
Out[53]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2109 | 0.008459 | 0.000521 | 3.606515e-06 | 0.489011 | 0.014703 | 0.0 | 0.101531 | 0.742215 | 0.000944 | 0.000000 | 0.022339 | 0.007341 | 0.000060 | 0.012552 | 0.000348 | 0.004059 |
| 2110 | 0.009717 | 0.000594 | 1.832925e-06 | 0.472527 | 0.027416 | 0.0 | 0.193424 | 0.726644 | 0.000992 | 0.006305 | 0.027228 | 0.008202 | 0.000047 | 0.014367 | 0.000402 | 0.007565 |
| 2111 | 0.004895 | 0.000302 | 8.352798e-07 | 0.357143 | 0.010709 | 0.0 | 0.110597 | 0.617647 | 0.000774 | 0.100000 | 0.026916 | 0.002917 | 0.000033 | 0.007157 | 0.000318 | 0.006417 |
| 2112 | 0.004622 | 0.000381 | 8.352798e-07 | 0.452381 | 0.010099 | 0.0 | 0.109903 | 0.707612 | 0.000745 | 0.100000 | 0.024805 | 0.003172 | 0.000010 | 0.007832 | 0.000309 | 0.007682 |
| 2113 | 0.006650 | 0.000570 | 0.000000e+00 | 0.410256 | 0.016235 | 0.0 | 0.126421 | 0.667820 | 0.001006 | 0.000000 | 0.026426 | 0.003908 | 0.000000 | 0.009226 | 0.000361 | 0.003041 |
In [54]:
columnsTitles = ['0','1','2','3','4','5','6','7','8','9','10','11','15','13','14','12']
All_inverse_T=All_inverse_T.reindex(columns=columnsTitles)
All_inverse_T =All_inverse_T[::-1]
All_inverse_T.head(5)
Out[54]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 15 | 13 | 14 | 12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2113 | 0.006650 | 0.000570 | 0.000000e+00 | 0.410256 | 0.016235 | 0.0 | 0.126421 | 0.667820 | 0.001006 | 0.000000 | 0.026426 | 0.003908 | 0.003041 | 0.009226 | 0.000361 | 0.000000 |
| 2112 | 0.004622 | 0.000381 | 8.352798e-07 | 0.452381 | 0.010099 | 0.0 | 0.109903 | 0.707612 | 0.000745 | 0.100000 | 0.024805 | 0.003172 | 0.007682 | 0.007832 | 0.000309 | 0.000010 |
| 2111 | 0.004895 | 0.000302 | 8.352798e-07 | 0.357143 | 0.010709 | 0.0 | 0.110597 | 0.617647 | 0.000774 | 0.100000 | 0.026916 | 0.002917 | 0.006417 | 0.007157 | 0.000318 | 0.000033 |
| 2110 | 0.009717 | 0.000594 | 1.832925e-06 | 0.472527 | 0.027416 | 0.0 | 0.193424 | 0.726644 | 0.000992 | 0.006305 | 0.027228 | 0.008202 | 0.007565 | 0.014367 | 0.000402 | 0.000047 |
| 2109 | 0.008459 | 0.000521 | 3.606515e-06 | 0.489011 | 0.014703 | 0.0 | 0.101531 | 0.742215 | 0.000944 | 0.000000 | 0.022339 | 0.007341 | 0.004059 | 0.012552 | 0.000348 | 0.000060 |
In [55]:
All_inverse_T = np.array(All_inverse_T)
In [56]:
All_inverse_T = trainMinMax.inverse_transform(All_inverse_T)
In [57]:
All_inverse_T
Out[57]:
array([[1.10500000e+04, 1.82345776e+06, 7.98626858e+02, ...,
1.01005364e+08, 2.23377243e+03, 4.48450527e+05],
[9.83200000e+03, 1.34492997e+06, 8.08548894e+02, ...,
9.08518825e+07, 2.22220199e+03, 5.71106414e+05],
[9.99600000e+03, 1.14548939e+06, 8.08548894e+02, ...,
8.59378578e+07, 2.22424209e+03, 8.41514530e+05],
...,
[7.06690000e+04, 8.83410322e+07, 1.18794958e+07, ...,
7.44612429e+08, 5.74445943e+03, 7.39894557e+09],
[9.98370000e+04, 9.13641667e+07, 1.18794958e+07, ...,
7.80214701e+08, 5.94215548e+03, 7.40803421e+09],
[1.02834000e+05, 2.72754401e+08, 1.18150952e+07, ...,
8.26556232e+08, 1.17095829e+04, 7.42680941e+09]])
In [58]:
predicted_btc_price = regressor.predict(X_test_3D)
predicted_btc_price2 = model.predict(X_test_3D)
In [59]:
inverse_T =np.concatenate((X_test_4N, predicted_btc_price), axis=1)
In [60]:
inverse_T = pd.DataFrame(inverse_T,columns=['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15'])
columnsTitles = ['0','1','2','3','4','5','6','7','8','9','10','11','15','13','14','12']
inverse_T=inverse_T.reindex(columns=columnsTitles)
inverse_T.head(5)
Out[60]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 15 | 13 | 14 | 12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.427382 | 1.000000 | 0.986381 | 0.382353 | 0.483978 | 0.473046 | 0.172909 | 0.382353 | 0.922934 | 0.910517 | 0.385649 | 0.355475 | 1.090726 | 0.368126 | 1.000000 | 1.000000 |
| 1 | 0.398563 | 0.262565 | 1.000000 | 0.225490 | 0.372166 | 0.339485 | 0.120980 | 0.225490 | 0.890929 | 0.931034 | 0.277162 | 0.363200 | 0.946243 | 0.300381 | 0.330390 | 0.931427 |
| 2 | 0.118091 | 0.250274 | 1.000000 | 0.029412 | 0.031412 | 0.335017 | 0.145508 | 0.029412 | 0.943769 | 0.931034 | 0.439168 | 0.060428 | 0.797729 | 0.248335 | 0.307437 | 0.898232 |
| 3 | 0.791627 | 0.273466 | 0.976479 | 0.519608 | 1.000000 | 0.441205 | 0.191333 | 0.519608 | 0.998692 | 1.000000 | 0.411170 | 0.103414 | 1.022931 | 0.342198 | 0.419870 | 0.849767 |
| 4 | 1.000000 | 0.131751 | 0.902079 | 0.754902 | 0.471743 | 0.444882 | 0.094961 | 0.754902 | 1.000000 | 1.000000 | 0.316039 | 1.000000 | 0.907855 | 0.248763 | 0.305134 | 0.823203 |
In [61]:
#change to real number
inverse_T = testMinMax.inverse_transform(inverse_T)
dataset.head(10)
Out[61]:
| activeAddresses | adjustedTxVolume(USD) | averageDifficulty | blockCount | blockSize | exchangeVolume(USD) | fees | generatedCoins | marketcap(USD) | medianFee | medianTxValue(USD) | paymentCount | price(USD) | realizedCap(USD) | txCount | txVolume(USD) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 102834.0 | 2.727544e+08 | 1.181510e+07 | 573.0 | 65641701.0 | 3.461536e+09 | 17.753439 | 14325.0 | 5.418036e+09 | 0.000221 | 75.837923 | 75246.0 | 88.39 | 4.632666e+09 | 26512.0 | 5.026659e+08 |
| 1 | 99837.0 | 9.136417e+07 | 1.187950e+07 | 557.0 | 53092124.0 | 2.765901e+09 | 16.292335 | 13925.0 | 5.317491e+09 | 0.000222 | 66.125885 | 76006.0 | 86.77 | 4.621039e+09 | 25088.0 | 1.995761e+08 |
| 2 | 70669.0 | 8.834103e+07 | 1.187950e+07 | 537.0 | 14846398.0 | 2.742631e+09 | 16.982480 | 13425.0 | 5.483492e+09 | 0.000222 | 80.628978 | 46219.0 | 89.50 | 4.615411e+09 | 23994.0 | 1.891868e+08 |
| 3 | 140714.0 | 9.404545e+07 | 1.176827e+07 | 587.0 | 123559210.0 | 3.295696e+09 | 18.271835 | 14675.0 | 5.656034e+09 | 0.000226 | 78.122568 | 50448.0 | 92.33 | 4.607194e+09 | 25967.0 | 2.400780e+08 |
| 4 | 162384.0 | 5.918720e+07 | 1.141644e+07 | 611.0 | 64268502.0 | 3.314849e+09 | 15.560253 | 15275.0 | 5.660144e+09 | 0.000226 | 69.606270 | 138655.0 | 92.42 | 4.602690e+09 | 24003.0 | 1.881442e+08 |
| 5 | 73882.0 | 7.553726e+07 | 1.141644e+07 | 599.0 | 14884957.0 | 3.857951e+09 | 15.615545 | 14975.0 | 5.431633e+09 | 0.000212 | 78.872088 | 49683.0 | 88.71 | 4.598286e+09 | 26033.0 | 2.290478e+08 |
| 6 | 77797.0 | 8.994418e+07 | 1.141644e+07 | 580.0 | 16160708.0 | 3.512862e+09 | 17.666264 | 14500.0 | 5.205337e+09 | 0.000192 | 77.999367 | 51084.0 | 85.04 | 4.587935e+09 | 26768.0 | 2.563719e+08 |
| 7 | 83650.0 | 1.552426e+08 | 1.079240e+07 | 588.0 | 18505439.0 | 4.195623e+09 | 19.729360 | 14700.0 | 5.212237e+09 | 0.000211 | 89.428500 | 53736.0 | 85.17 | 4.578644e+09 | 29731.0 | 3.004893e+08 |
| 8 | 105006.0 | 1.763392e+08 | 1.068695e+07 | 608.0 | 24619430.0 | 6.206102e+09 | 28.658120 | 15200.0 | 4.665896e+09 | 0.000224 | 130.835753 | 62947.0 | 76.26 | 4.572197e+09 | 39794.0 | 4.116363e+08 |
| 9 | 94751.0 | 1.680972e+08 | 1.068695e+07 | 630.0 | 24165146.0 | 4.161363e+09 | 30.859781 | 15750.0 | 3.713027e+09 | 0.000226 | 60.685189 | 59689.0 | 60.70 | 4.544131e+09 | 35034.0 | 4.544110e+08 |
In [62]:
inverse_T =pd.DataFrame(inverse_T)
In [63]:
print(inverse_T.shape)
inverse_T.head(5)
(60, 16)
Out[63]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 102834.0 | 2.727544e+08 | 1.181510e+07 | 573.0 | 65641701.0 | 3.461536e+09 | 17.753439 | 14325.0 | 5.418036e+09 | 0.000221 | 75.837923 | 75246.0 | 97.027085 | 4.525531e+09 | 39794.000000 | 5.026659e+08 |
| 1 | 99837.0 | 9.136417e+07 | 1.187950e+07 | 557.0 | 53092124.0 | 2.765901e+09 | 16.292335 | 13925.0 | 5.317491e+09 | 0.000222 | 66.125885 | 76006.0 | 89.690198 | 4.514045e+09 | 25718.805539 | 4.716270e+08 |
| 2 | 70669.0 | 8.834103e+07 | 1.187950e+07 | 537.0 | 14846398.0 | 2.742631e+09 | 16.982480 | 13425.0 | 5.483492e+09 | 0.000222 | 80.628978 | 46219.0 | 82.148698 | 4.505220e+09 | 25236.335600 | 4.566019e+08 |
| 3 | 140714.0 | 9.404545e+07 | 1.176827e+07 | 587.0 | 123559210.0 | 3.295696e+09 | 18.271835 | 14675.0 | 5.656034e+09 | 0.000226 | 78.122568 | 50448.0 | 93.584459 | 4.521135e+09 | 27599.674751 | 4.346648e+08 |
| 4 | 162384.0 | 5.918720e+07 | 1.141644e+07 | 611.0 | 64268502.0 | 3.314849e+09 | 15.560253 | 15275.0 | 5.660144e+09 | 0.000226 | 69.606270 | 138655.0 | 87.740894 | 4.505293e+09 | 25187.918283 | 4.226409e+08 |
In [64]:
inverse_T = np.array(inverse_T)
type(inverse_T)
Out[64]:
numpy.ndarray
In [65]:
inverse_T= inverse_T[:,12]
In [66]:
all_pred=All_inverse_T[:,12]
all_pred = all_pred[::-1]
MSE¶
In [67]:
#score for reulu
from sklearn.metrics import mean_squared_error
mean_squared_error(X_train_4_graph,All_inverseRulu_T)
Out[67]:
20.027729697685217
In [68]:
plt.figure(figsize=(50,50),dpi=80)
plt.plot(X_train_4_graph, color = 'red', label = 'Real LTC Value',linewidth=3, linestyle="--")
plt.plot(All_inverseRulu_T, color = 'green', label = 'Predicted LTC Value')
plt.title('litecoin Value Prediction' )
plt.xlabel('Days')
plt.ylabel('litecoin Value')
plt.legend()
plt.show()
In [69]:
#score $7.50 difference
from sklearn.metrics import mean_squared_error
mean_squared_error(X_train_4_graph,all_pred)
Out[69]:
7.492208642235717
In [70]:
plt.figure(figsize=(50,50),dpi=80)
plt.plot(X_train_4_graph, color = 'red', label = 'Real LTC Value',linewidth=3, linestyle="--")
plt.plot(all_pred, color = 'blue', label = 'Predicted LTC Value')
plt.title('litecoin Value Prediction' )
plt.xlabel('Days')
plt.ylabel('litecoin Value')
plt.legend()
plt.show()
In [71]:
plt.plot(y_test_4_graph, color = 'red', label = 'Real LTC Value' ,linewidth=.5, linestyle="--")
plt.plot(inverse_T, color = 'blue', label = 'Predicted LTC Value', linewidth=3)
plt.title('litecoin Value Prediction')
plt.xlabel('Days')
plt.ylabel('litecoin Value')
plt.legend()
plt.show()
In [ ]: